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Citi GPS: Global Perspectives & Solutions

January 2019

CAR OF THE FUTURE v4.0The Race for the Future of Networked Mobility

ZR70082

Sticky Note

© 2019 Citigroup

Itay Michaeli U.S. Autos & Auto Parts Analyst

+1-212-816-4557 | [email protected]

Justin Barell U.S. Autos & Auto Parts Analyst


Jamshed Dadabhoy India Autos & Consumer Analyst

+65-6657-1146 | [email protected]

Kota Ezawa Japan Industrial & Consumer Electronics Analyst

+81-3-6776-4640 | kota.ezawa @citi.com

Raghav Gupta-Chaudhary Europe Autos & Machinery Analyst

+44-20-7986-2358 | [email protected]

Manabu Hagiwara Japan Autos & Auto Parts Analyst


Ethan Kim Korea Autos, Logistics & Capital Goods Analyst


Arthur Lai Greater China Technology Analyst

+852-2501-2758 | arthur.y.lai @citi.com

Beatrice Lam China Autos Analyst

+852-2501-8455 | [email protected]

Atif Malik U.S. Semiconductor & Semiconductor Equipment Analyst


Jonathan Raviv U.S. Aerospace & Defense Analyst


Jim Suva, CPA U.S. IT Hardware & EMS and Telecom & Networking Equipment Analyst


Angus Tweedie Europe Autos & Auto Parts Analyst

+44-20-7986-3949 | [email protected]

Christian Wetherbee U.S. & Canada Airfreight, Land, Surface & Marine Transportation Analyst


Arifumi Yoshida Japan Autos & Auto Parts Analyst


U.S. Auto & Auto Parts Research Team

U.S. Auto & Auto Parts Research Team

Global Head of Citi Digital Strategy

Citi Digital Strategy

Citi Digital Strategy

January 2019 Citi GPS: Global Perspectives & Solutions

© 2018 Citigroup

3

CAR OF THE FUTURE v4.0 The Race for the Future of Networked Mobility If you were asked to think outside the box and give your vision of the future there

are two things you would need to do. First, you would need to categorize those

things that you think will stay constant throughout time, next, you would think of

those things that will change. On the constant side, you may believe you’re always

going to live in a house-like structure on land versus living underwater, or that you’re

always going to wear clothes and not a digital outfit projected from your cell phone.

On the change side, however, you probably have a mix of things you envision could

be improved in the future.

Looking back at old futuristic movies and television shows, the creative people who

made them did those same two things. Interestingly, most believed how we were

going to traverse this planet would be vastly different. Be it some type of Star Trek

transporter that digitizes your molecules and sends them hurtling through space, or

a personal spaceship that you use to commute, the future thinkers in Hollywood

didn’t start their shared vision resigned to the idea they would be jumping in a car

and driving themselves to their next destination.

So how close are we to ditching our personal cars in the future? While we may not

be up to personal flying taxis yet, it does seem that reality may finally be catching up

with the hype. A handful of companies are pursuing various level-4 RoboTaxi

services (where the car is totally in control and humans are just passengers) to build

urban rideshare networks in the coming one to three years. These are being

planned for cities and surrounding suburbs and the race to launch and

commercialize these RoboTaxi’s is all about building a powerful network effect. This

network effect is determined by who can introduce and scale safe, reliable, fast, and

low-cost urban RoboTaxi fleets.

But there’s more to come. Around 2020-2021 we expect to see more autonomous

vehicle (AV) features sold on personal cars — like vehicles that can drive

themselves on highways — and offered in the same way advanced safety options

are today. In the early/mid 2020s, we see the expansion of AVs into personally-

owned vehicles that consumers can subscribe to. An AV Subscriber network

attempts to preserve the value of instant-car-access “ownership” with a shared

network. A ‘lease’ payment for an AV Subscriber would include use of the car, plus

insurance and maintenance. In addition to extra AV safety features on this car, the

car will drive itself to get serviced in the middle of the night or a new car with

enough seats to pick up the whole family at the airport can be sent to your house

overnight. The consumer can also decide to leverage the network platform for peer-

to-peer sharing and have their car make money when it’s not being used.

Ultimately, we see the RoboTaxi networks and the AV Subscriber network

integrating together and once you own the network, new forms of mobility can be

integrated — such as “flying cars” operating on certain routes. We estimate the U.S.

high-population-density urban RoboTaxi addressable market (TAM) alone could

exceed $350 billion — with high margins for the network leaders — yielding a nearly

$1 trillion enterprise value create at 15x EBIT (earnings before interest and tax). We

also we see the market for Tier-1 suppliers in advanced driver-assistance systems

and autonomous vehicles rising to > $100 billion by 2030E from the current $5-6

billion today and the post 2021 adoption curve for AV being steeper than expected.

With cars sorted out, what else should we change in the future?

Kathleen Boyle, CFA

Managing Editor, Citi GPS

Close to Tipping Point on Car of the FutureWE BELIEVE LEVEL 4 DRIVERLESS CAR ADOPTION WILL BE STEEPER THAN CONSENSUS EXPECTS

Advanced Driver Assistance Systems (ADAS) market

THE PATH FOR AUTOS FROM CONSUMER PRODUCTS TO STRATEGIC NETWORKS

$5-6 billion

Complex city makes it easier to recoup initial very expensive AV costs

Greatest impact on pollution and congestion; total addressable market ~$900bn

Conquering complex domains = faster scaling later in ‘easier’ domains

Scaling easier if complex cities are conquered first

Urban/Suburban miles = 1.5trn (~50% of total U.S. miles driven)

RoboTaxi covers major cities and surrounding population centers (commuting) AV sensor costs decline

enough to sell L4/L5 as a vehicle option (like ADAS)

Integrate RoboTaxi + OEM App network into broader subscription and P2P network

More robust network = greater share of Personal AVs

Rideshare business becomes more asset light (source AVs from consumers too)

AV owners make money renting to rideshare, P2P, or subscription service

Non-AV owners can still access network (OneApp for rideshare, rentals)

Early-to-Launch RoboTaxi AV Network (In Complex Cities)

Expand Network to Cover Most Urban/Suburban Miles

Expand Network to AV Subscribers (High Volume)

Achieve Virtuous Loop of an Integrated Mobility Network

2019 EarlyScale Network to Achieve “Escape Velocity”

Early/Mid Mid Late

Faster urban scaling = more data = better safety track record = competitive edge

Faster urban scaling = higher load factor (dedicated AVs with partitions)

Higher load factor = lower user costs = higher usage = larger network effects

>$100 billion

2030E

$38 billion

2025E

Today

2020s:>

> > > >

© 2019 Citigroup

THE AUTO INDUSTRY WILL BE CHARACTERIZED BY FOUR TYPES OF VERTICALS2030

T W O

AV SUBSCRIPTIONS 2030

T H R E E

ROBOTAXI /AV SUBSCRIPTION INTEGRATED NETWORK

2030

TRADITIONAL OWNERSHIP

F O U R2030

2030O N E

ROBOTAXIS

(URBAN/SUBURBAN)

MOBILITY-ON-DEMAND combined with micro-mobility solutions operating in mainly urban and some suburban markets

DRIVERLESS-CAPABLE CARS that people subscribe to combining the best attributes of personal ownership with the benefits of AVs

A COMBINATION OF ROBOTAXI’S AND AV SUBSCRIPTIONS as their networks narrow to provide integrated solutions

CERTAIN VEHICLE SEGMENTS (pick-ups, commercial vehicles) but could still have AV features as standalone options

Traditional Ownership

Or AV Subs

Traditional Ownership

Or AV Subs

AV SubsOr

Traditional Ownership AV Subs

RoboTaxiOr

Micro-Mobility

RoboTaxiOr

Micro-Mobility

RURAL (SNOW) RURAL (WARM)

Crossovers

CITY (SNOW) CITY (WARM)

Pickup Trucks 3rd Row SUVs/Vans

MOBILITY PREFERENCES IN THE FUTURE WILL VARY WIDELY BETWEEN REGIONS – I.E. URBAN VS. RURAL AND GOOD VS. BAD WEATHER – AFFECTING VEHICLE SALES

Colors signify risk to auto sales:

>

no/minimal risk some risk significant risk of lower vehicle sales

Citi GPS: Global Perspectives & Solutions January 2019

© 2018 Citigroup

6

Contents Car of the Future v4.0 7

Transforming Mobility As We Know It 18

Urban RoboTaxi 24

The Rise of Micro-Mobility 39

Spotlight on Ridesharing in India 48

AV Subscriptions 49

It All Started with ADAS…. 62

The Auto Industry 2030+ 69

AV Technology—Building an AV 77

Profile of Major Automakers 94 Tesla Case Study 100

Korean Autos: Where They Stand in Autonomous Driving Long-Term Megatrend? 104

Japan Autos 112

Connectors/Sensors: A Major Beneficiary of Vehicle Electrification 121

Autonomous Trucks 130

(Flying) Car of the Future 134

Mobility Ecosystem Changes: Implications for Corporate Treasury 139


© 2018 Citigroup

7

Car of the Future v4.0 When we began our Car of the Future series several years ago, the theme was

mostly defined by regulatory-driven technology entering the car (turbochargers,

stop/start systems) and to some extent vehicle connectivity opening up new

revenue streams. Most often, the providers of automotive content gained at the

expense of automakers, and content grew in linear fashion over many years. Today,

the Car of the Future theme is much more than that, both in terms of the potential

impact of emerging technology to re-shape the industry, and the historic alignment

of stakeholder interests to push ahead. Regulators now see step-function

opportunities to address road safety, congestion, pollution, and inequality.

Companies — both Auto and Tech — see opportunities not only to meaningfully

expand revenue but to completely redefine the personal mobility business model

through newly created networks. Consumers are demanding solutions for safety,

convenience, enjoyment, and more. And the perceived threat from new industry

players has sparked an industry race the likes of which we haven’t seen before.

When thinking about innovations such as artificial intelligence (AI), connectivity,

electrification, and big data, there’s perhaps no more obvious use case than the Car

of Today. The age of mass-market personal cars solved many of yesterday’s

mobility problems, but also created new ones such as congestion, pollution, and

poorly utilized urban infrastructure. And vehicle safety, while vastly improved,

remains a substantial societal and economic problem that unfortunately is not

easing in the age of distracted driving. The next five or so years will likely

commence a new automotive era that will not only see the Car of the Future

address many of these problems, but redefine what the “car” actually is.

The tipping point for all of this will be the entry of fully autonomous vehicles (AVs)

over the next few years, initially operating in specific pre-defined domains, or “level-

4”. Even under these restricted level-4 domains, we believe powerful network

effects can start forming. This is because the entry of AVs will begin to morph the

“car” from a consumer product into a network — a network you can access on-

demand or as a subscriber, often cheaper and more convenient than some of

today’s modes of personal transportation. We expect this to occur in various stages,

each of which will redefine a part of the industry. Electric vehicles (EVs) will be

important in this race — an EV sold without AV capabilities will not be competitive,

and vice-versa.

Five key takeaways:

1. The most coveted asset in the Car of the Future race is the AV network effect

itself, both at the mobility provider level and at the supplier level (complex

systems/software). We estimate the U.S. high-population-density urban

RoboTaxi addressable market (TAM) alone could exceed $350 billion — with

high margins for the network leaders — yielding a nearly $1 trillion enterprise

value at 15x EBIT (earnings before interest and tax). We view AV Subscriptions

(or AV Subs, which we like to refer to as “Ownership 2.0”), as another

compelling business model for non-urban markets, which also promises to

expand the profit pool by re-defining the automotive value chain while

monetizing shared platforms such as peer-to-peer.

2. Peak Auto? Hardly. The profit potential of the “car” is more likely at its earlier

stages. Besides new revenue streams from areas like data platforms, car-

sharing and time-spent-in-car, it is often overlooked that automakers today miss

out on a large part of a car’s lifetime profit stream. AVs (propelled by EVs) have

the power to totally change the automotive value chain.

“AV is the biggest thing since the

Internet”

- GM, November 2017

Figure 1. ADAS Market Size (Tier-1 Level)

BCG = Boston Consulting Group Source: Company Reports, Citi Research

ADAS Market Size Now 2022E 2025E 2030E

BCG $5 $13 - -

Veoneer $5 $15 $30 -

Citi $5 $18 $41 $111


© 2018 Citigroup

8

3. The forces of the network effect will likely mean that some automakers,

perhaps many, could ultimately find themselves left behind. New players are

likely to emerge and are in fact already emerging. The 2030+ industry outcome

could see several automaker laggards, but a few (potentially very large)

winners.

4. We believe the post 2021 level-4 AV adoption curve could end up proving much

steeper than consensus expects. For suppliers, the current $5-6 billion

ADAS/AV market could reach ~$111 billion by 2030E, which we believe is far

above consensus. That said, suppliers are not as directly exposed within the

urban RoboTaxi vertical given inherently “low” volume by auto standards

(contract auto manufacturers being an exception), so much of this growth

depends on non-RoboTaxi verticals like AV Subs.

5. Contrary to the popular narrative, the impact of this change will be felt very

differently depending on region (city vs. rural), weather (snowfall intensity), and

vehicle segmentation (utility trucks vs. sedans/SUVs). These considerations

alone carry significant relative investment implications that are mostly

overlooked today, even though they are actually quantifiable.

Welcome to the Car of the Future v4.0!

Figure 2. AV Network Timeline

Source: Citi Research


© 2018 Citigroup

9

Figure 3. Huge Variations of Mobility and Impact by Region (x-axis) and Segment (y-axis)

(Green = Limited/no risk to auto sales, Yellow = Some risk, Red = Significant risk)


Investing in Mobility 2030

As a general framework, we think the investment considerations for the Car of the

Future boil down to optimizing for two factors: defensive and offensive exposure to

various AV network verticals.

For automakers:

1. Defensive Traits: Defensive traits are those least likely to see a disruptive

change from networked mobility in the coming years. In our view, least affected

will be businesses concentrated in rural regions and vehicle segments that are

used for commercial/utility purposes such as pickup trucks, large SUVs (3rd

row), large vans, and certain specialty vehicles. Additionally, businesses

concentrated in colder/snowier weather regions will likely be considerably

slower to adopt change due to network reliability issues and to some extent EV

range issues. To be sure, these regions and segments will see more

electrification (including EVs) and automated driving features — exposed

companies will need strong capabilities in each — but they are the least likely

to be fundamentally disrupted by Car of the Future trends. Think of these as the

“safest” exposures for Auto companies. On the flip side, exposures in cities,

sedans, and warm weather regions will likely see the greatest change. These

are the “riskiest” exposures within the Autos segment.

2. Offensive Traits: At the automaker level, we look for two important traits: (1)

Who is well-positioned to rapidly deploy an urban RoboTaxi AV network (also

serving as a foundation for micro-mobility and eventually even aerial vehicles),

an AV Subscription model, or both?; and (2) Because electric vehicles (not the

main focus of this report, but our full Electric Vehicle Citi GPS report from 2018

can be found here) will be integral to making AV networks more competitive, we

look for automakers with strong EV technology and a financial incentive to

deploy EVs rapidly. The AV and EV themes today are generally analyzed

separately by investors, but we think companies eventually need both. An EV

without AV/shared capabilities eventually won't be competitive, while an

AV/shared vehicle that isn’t an EV won't either.

https://ir.citi.com/YeuPrmaSwsHiJjNFbnzOPduuNx5m67lbSNYNShXQV7RE9q8GSAwWsHHad4cXQ21TjyZopYAUJIM%3d


© 2018 Citigroup

10

For suppliers:

1. Defensive Traits: It really comes down to having content that will remain

relevant in the car. This isn’t always a straightforward exercise since some

content could gain prominence before theoretically being de-contented (certain

powertrain systems, some passive safety, even mirrors). Given our view around

the potential for future changes in the automotive value chain, we tend to prefer

original equipment manufacturer (OEM) exposure over aftermarket, though this

doesn’t necessarily hold true for all (i.e. tires better positioned vs.

braking/powertrain).

2. Offensive Traits: Our philosophy to Car of the Future supplier investing has

boiled down to a simple framework: (1) Who is helping automakers achieve

strategic, financial and regulatory goals and who is helping the automakers sell

more cars?; and (2) Where is the greatest room for technology and

manufacturing differentiation? We see a number of areas here, and not all are

necessarily high-tech. First, given the importance of the AV network race, we

look for suppliers best exposed to deliver systems/software solutions,

particularly around driving policy. We also look for suppliers with electrical

architecture/electronics expertise required not only to enable these complex

vehicles but to also optimize for robustness, costs, and weight (for example,

central domain controllers replacing distributed electronic control units (ECUs)).

We also look for derivative impacts. One example is the need for more complex

cockpit electronics for driver-monitoring systems and digital clusters that aid

driver situational awareness. Another example comes from automakers

increasing outsourcing as they redeploy capital away from areas that were

traditionally insourced (and arguably make less sense to insource going

forward) — stamping being one example and to some extent

transmission/driveline systems. That being said, we view supplier AV-related

exposure as less exposed to the urban RoboTaxi vertical (low volume by

automotive standards) and more to trends within advanced driver assistance

systems (ADAS) and eventually AV Subs. In terms of timing, we believe 2020-

2022 could see an upward inflection for growth of certain automotive content —

a growth phase that could last for the entire decade — driven by:

• Several high-volume EV launches from major automakers beginning in

2020-22 driving EV-related content ranging from propulsion to electrical

architecture and advanced electronics;

• A new wave of ADAS regulations expected to be implemented around 2020,

which should drive content. Related to that, we also expect a number of

high-volume level-2+ and level-3 automated driving systems. For example,

GM expects to roll out its next generation SuperCruise feature

(“UltraCruise”) to non-Cadillac brands after 2020. FCA is expected to launch

level-2+ in 2020, while the BMW-FCA-Intel (and others) venture is expected

to launch level-3 systems in 2021.

• The increased complexity of EVs and automated driving features will likely

drive an inflection for advanced electrical architectures inclusive of domain

controllers, OTA, cybersecurity and advanced cockpit electronics content.

• 2021 is also expected to see additional level-4 deployments from a number

of global industry players. If our assessment on business models like AV

Subs is correct, 2020-22 will mark the beginning of an era that starts seeing

rapid penetration of level-4.


© 2018 Citigroup

11

Figure 4. Auto Technology Investing Framework

Source: Citi Research Note: x-axis represents areas of risk to the coveted network effect, y-axis represents the range from automakers to suppliers

Tracking the Car of the Future—AV Mobility Race

Automakers/Mobility Providers

Relative to the big picture, we think there’s a bit too much emphasis on whether

companies will meet their exact “RoboTaxi” commercial deployment timetables.

Though an early-mover advantage is indeed an important component of the network

race, urban RoboTaxis are an unprecedented endeavor that are anything but easy

to precisely time. To summarize, key timetables many are watching include:

GM-Cruise is expected to launch an urban RoboTaxi service in late-2019;

Waymo was expected to launch a RoboTaxi service in late-2018, which occurred

under Waymo One but not yet at level-4, and on a limited deployment. It is

unclear at the moment when Waymo intends to proceed towards level-4;

Aptiv’s test fleet is expected to remove drivers in late-2019/early 2020,

Tesla is expected to launch additional AV features in 2019/20 — however, we do

not view Tesla a player in the urban RoboTaxi market,

Zoox is expected to launch an urban RoboTaxi service by year-end 2020; and

Ford is expected to launch an urban RoboTaxi service in 2021

Audi (AID) also expects to deploy urban RoboTaxis in 2021

Could some or even all deployments end up being delayed? Sure, but it’s important

to remember a few things:

Network

Effect

Automakers

Supply Chain

At

Risk Commoditized/Competitive Solidly

Growing

RoboTaxi

AV/EV Subs

NA Pickup Trucks

Non-urban

Sedans &

Crossover SUVs

Sedans + Crossovers

in Cities

(Warm Weather

particular)

3rd Row SUVs/Vans

AV Software/Systems

Seating Content

Electrical Architectures & Electronics

Sensors/DMS

Outsource Impact (Stamping)

Cockpit Electronics

Certain Aftermarket

Certain Powertrain

EV related


© 2018 Citigroup

12

1. Patience can pay off, particularly for what we believe will emerge as a very

large AV network addressable market. Remember, Mobileye went through an

~8 year journey prior to reaching series production because it chose to focus

on the more challenging problem (monocular camera as opposed to stereo),

which ultimately led to a lasting competitive advantage;

2. Similarly, over the years we have seen a number of false starts for the inflection

of EVs. The long-awaited EV inflection took longer than expected, but never

derailed. And unlike EVs that require a major supply chain overhaul to scale,

we think AVs have the prospects to scale much faster;

3. To that point, once you conquer the highly complex urban AV domain,

establishing “escape velocity: for an AV network could happen relatively quickly.

For example think of the rapid adoption we have seen this year in the e-scooter

market (Bird, Lime, Jump, etc). As discussed later in this report, the AV race is

equally important at the pre-launch and post-launch (scaling) phases. If every

company were to hypothetically delay launch for 1-2 years (not that we are

expecting that), but that delay meant scaling would then prove more rapid, then

the delay would mean very little in the long-run. Of course, if one company

accelerated while another delayed, that would carry investment implications.

So the good news for AV bulls and bears alike is that they are both right. Bears are

right to point out a true driverless world (level-5 autonomy) is probably many years,

if not a decades, away. And they are also right to point out there are some signs

suggesting level-4 deployment could get pushed to the right a bit. But we believe

bulls rightly point out that level-4 has become more a matter of when, not if, and that

the level-4 opportunity alone is enormous without needing level-5.

As a final point, looking at launch timing alone can be misleading because a

network could launch a “watered-down” network in terms of size and capability,

even if that network is driverless. Time will tell whether 2019 will indeed see the

industry’s first true level-4 commercial deployment in major arenas, or whether this

will end up more of a 2020-2021 event. Either way, we think it’s important to keep in

mind the importance of the post-launch phase, so below we have included a post-

launch checklist of sorts, which we cover in more detail later in the report.

Figure 5. RoboTaxi AV: Key Players & Pre-Launch Assessments

Pre-AV Launch Assessment

Pursuing U.S. Urban RoboTaxi?

L4 Launch Date

L4 Test Fleet Size?

Testing in Actual U.S.

Cities?

AV Headcount Annual Spend

Purpose Built AV?

Is AV an EV? AV Mfg. Integration

Waymo Yes 2018-19 ~800 Yes, Phoenix <1k Unknown Not entirely No 2 OEM Partners

GM-Cruise 2019 (SF) 180 Yes, San Fran ~1.6k ~$1bn Yes Yes GM-Cruise

Zoox Yes ~2020 Unclear San Fran >500 Unknown Yes Yes Building its own

Aptiv Yes. For customers YE'19/early-20 ~100 (~150 YE'18) Yes, Vegas Unknown ~$160mln No No Tier-1 to OEMs

Ford Yes 2021 (Miami) 120 Yes, Miami Unknown ~$500mln Yes No Ford-Argo AI

Tesla Unlikely 2019-2020 Tesla Installed Base

Less Exposure

Unknown Unknown No Yes Tesla

FCA Not Apparent 2021 ~40 (~80 YE'18) Not Apparent - Intel/Mobileye etc. consortium- ---Supplying Waymo--- FCA-Waymo

Daimler Possible (EU 1st?) Early next decade

Unclear Cali in 2019 -- Bosch AV co-develop-- Expected Expected Daimler

VW/Audi Possible (EU 1st?) 2021 (urban) Unclear Not Apparent Unknown Unknown Expected Expected VW/Audi

BMW Possible (EU 1st?) 2021 ~40 (~80 YE'18) Not Apparent - Intel/Mobileye etc. consortium- Expected Expected BMW

Honda Yes (GM-Cruise partner)

Unclear (ex. Cruise)

Unclear Not Apparent Unknown Unknown Yes Yes Honda

Nissan Apparent (Japan 1st?) Early-2020s Unclear Japan Unknown Unknown Expected Expected Nissan

Toyota Not Apparent 2023 Unclear Not Apparent Unknown Unknown Expected Expected Toyota-TRI

Zenuity No 2021 (hwy) 100 Not Apparent >500 Unknown OEM customers



© 2018 Citigroup

13

Figure 6. RoboTaxi AV Post Launch Checklist


There is one area where we do agree with AV skeptics and this also goes into how

we track Automakers/Mobility providers through this network race.

One could argue the AV industry might be putting too many eggs in the urban

RoboTaxi basket — not just because it is such a difficult engineering feat, but also

because it might prove to be a ‘few-winners-take-all’ market in a particular region.

Although we agree with GM that AVs are the biggest thing since the Internet, we

question why the industry is seemingly pursuing only two AV verticals — RoboTaxis

and Highway Features — the latter being far less interesting because it doesn’t

create a visible network effect. We are not necessarily advocating companies

expand/shift resources towards less-complex AV domains, unless of course those

domains offer compelling business models. As discussed later, we view AV

Subscriptions (AV Subs) as another compelling AV business model that is not as

much of an engineering moonshot as urban RoboTaxis. To be sure, we do see

significant value in training AVs in the harshest, most difficult domains regardless of

when RoboTaxis deploy. But unless you are truly in a position to win the urban

RoboTaxi race, other AV models should be considered as well only because nobody

truly knows when that last 0.1% of AV accuracy will be achieved. So we look for

companies who appear to be planning more ‘outside the level-4 box’. Who is

pursuing peer-to-peer sharing? Who has large dealer networks? Who has

developed strong level-2/3 capabilities and partnered with leading suppliers? Who

has strong RoboTaxi AV assets that can complement?

Options for Going Outside Geo-fence?

Date of Deployment

Size of Launch Fleet?

Complexity Score (Fixed Route? Radius? Unprotected Left Turns?)

Agility Feedback?

Scaling Plans?

Post RoboTaxi AV Launch Checklist


© 2018 Citigroup

14

For Suppliers

Tracking auto supplier progress can be confusing at times. On the one hand,

investors obtain fairly good visibility with respect to which supplier is winning new

business contracts, as most auto suppliers periodically disclose business backlogs.

If a supplier claims to have good technology, it is fairly easy to assess whether

those claims are backed by automaker awards, which typically occur a few years

prior to the start of vehicle production.

But it is this sense of visibility that often creates traps for investors. Where AutoTech

investing often goes wrong is with the common notion that AutoTech penetration is

predictably linear, as those supplier backlogs often imply. The assumption can be

true in a longer-term setting — i.e. all cars will have XYZ feature by a certain year.

But in the shorter-term — often measured in years — the penetration of a feature

can be lumpy because most features are first sold to consumers as optional

equipment or as trim-level dependent features (i.e. Navigation only available only on

the “platinum” version of a vehicle). This means the penetration of various

technologies is more at the mercy of short-term macro influences than commonly

believed — creating an evolving intersect of sorts between cyclical and secular

forces. For example, if one is extremely bearish about auto pricing and/or the

broader economic cycle, even the most secularly positioned technologies could

suffer from reduced short-term penetration by virtue of consumers trading down in

option packages or trim packages. Or not. Perhaps we have reached a point where

consumer demand for these technologies is akin to certain consumer electronics

trends. At the very least, this is a concept that is important to understand when

forecasting financial results, but it is also an important concept to help us gauge

push vs. pull demand for various technologies.

The problem historically is that tracking real-time AutoTech take-rates and vehicle

trim mix is notoriously difficult. Sales volume data (SAAR or seasonally-adjusted

average annual rate of sales) is readily available to investors, but not the breakout

of trims and options equipped on those vehicles. This lack of available trim/feature

tracking data prompted us to spend over two years developing our own proprietary

tracker using big data and internally developed algorithms, which we first published

in April 2018 under the AutoTech//Tracker LIVE! product.

Figure 7 delves into our data (sorted by published vehicles) looking at trends in

different trim buckets. We view this as an important tool to track how varying macro

conditions influence consumer behavior with respect to AutoTech. Consequently,

this has implications for how we view auto suppliers in the context of the Car of the

Future theme.


© 2018 Citigroup

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Figure 7. Citi AutoTech//Tracker LIVE! Sample Dataset of Low/Mid/High Trim Levels

Source: Company Reports, Citi Research

How Might a Recession Influence the Car of the Future Landscape?

The global economy enters 2019 with pockets of weakness and increased

uncertainty. Car of the Future is of course a long-term theme, but significant

macroeconomic swings could conceivably shape the competitive playing field. For

example, we often recall Mobileye attributing some of its competitive success to

having been well-resourced to invest through 2008-09, while others cut back.

So if the global economy were to take a material turn for the worse in 2019, we can

see three ways in which that could impact the Car of the Future landscape:

1. Well-funded RoboTaxi Leaders Could Benefit: Developing urban RoboTaxis

has increasingly proven to be a more complex and expensive endeavor

requiring sizable test fleets and various infrastructure — in other words billions

of dollars of investment. An economic recession would likely slow down less-

funded industry players, eliminate some, and cause others to perhaps

temporarily pivot towards less intensive efforts such as aftermarket level-2/3

systems or very narrowly defined RoboTaxi domains (like age-restricted

communities). Well-funded players who are already in advanced testing —

arguably Waymo and GM-Cruise — could stand to possibly benefit from such a

scenario.

2. M&A: Automotive public equity multiples contracted significantly in the second-

half of 2018 on global economic pressures. A continuation of this trend into

2019 could conceivably spark opportunistic M&A, for three reasons. First,

strategic buyers could try to take advantage of lower multiples to position for

the 2020-2022+ growth inflection we described above. Second, the generally

healthier state of automotive balance sheets today (as compared to 2008-09)

could spark greater interest and/or allow acquirers to invest more aggressively


© 2018 Citigroup

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during a downturn, particularly if they see a large disconnect between

fundamentals and valuations. Third, the Tier-1 ADAS supplier market has

become a bit more fragmented in recent years, and it has been our prior view

that the AV landscape likely will not accommodate as many competitors due to

the sheer complexity of AV development. M&A here could conceivably entail

Tier-1 suppliers buying startups or perhaps even some consolidation amongst

the Tier-1 suppliers themselves, in cases where complementary capabilities

exist.

3. A True Test of Consumer Demand: Similar to the above discussion on trim

mix, a recession would allow industry observers to better understand consumer

demand for new technologies such as ADAS and semi-autonomous systems.

This could have consequences for how quickly automakers proceed to launch

more advanced technology, and how investors evaluate companies through

economic cycles. Incidentally, this test wouldn’t just be limited to assessing

individual vehicles’ trim/feature penetration, but also to demand for electric

vehicles, most notably the Tesla Model 3 because of its higher volume.

Figure 8. AV Value Chain: Select Companies Participating in Various Areas of the AV Value Chain (List Includes a Sample of Companies)


- Waymo- GM-Cruise- Rideshare Cos- Zoox- Ford- Daimler- Audi/VW

- GM-Maven- Ford- Turo- Getaround

- Most Automakers


© 2018 Citigroup

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Figure 9. Global ADAS – to Level 4 Penetration & Tier-1 Supplier Revenue TAM Forecast (LVP = Light Vehicle Production, Analysis for Personal

Retail Vehicles, Excludes Urban RoboTaxi TAM)


2019E 2020E 2021E 2022E 2023E 2024E 2025E 2026E 2027E 2028E 2029E 2030E 2031E 2032E

2019E 2020E 2021E 2022E 2023E 2024E 2025E 2026E 2027E 2028E 2029E 2030E 2031E 2032E

ADAS- AV Feature TAM

ADAS Penetration (%)

ADAS - Basic 34% 44% 41% 40% 27% 23% 21% 19% 17% 12% 12% 12% 12% 12%

ADAS + Level 2(+) 1% 1% 10% 12% 30% 35% 40% 40% 40% 40% 35% 30% 28% 28%

ADAS + Level 3+ (hwy L4) 0% 0% 1% 1% 2% 3% 3% 3% 3% 3% 3% 3% 3% 3%

L4 Features & AV Subs (Stage 1) 0% 0% 0% 1% 1% 1% 1% 3% 5% 10% 15% 20% 22% 22%

Total ADAS Penetration 35% 45% 52% 53% 60% 62% 65% 65% 65% 65% 65% 65% 65% 65%

No ADAS 65% 55% 48% 47% 40% 38% 35% 35% 35% 35% 35% 35% 35% 35%

L3-L4 Premium Penetration 1% 2% 16% 11% 29% 40% 42% 61% 79% - - - - -

ADAS Penetration (units)

Global LVP 100 100 100 100 100 100 98 96 94 92 90 89 87 87

No ADAS 65 55 48 47 40 38 34 34 33 32 32 31 30 30

Global ADAS Penetration 35 45 52 53 60 62 64 62 61 60 59 58 56 56

YoY 29% 16% 2% 13% 3% 3% -2% -2% -2% -2% -2% -2% 0%

ADAS - Basic 34 44 41 40 27 23 21 18 16 11 11 11 10 10

ADAS + Level 2(+) 1 1 10 12 30 35 39 38 38 37 32 27 24 24

ADAS + Level 3+ (hwy L4) 0 0 1 1 2 3 3 3 3 3 3 3 3 3

L4 Features or AV Subs (Stage 1) 0 0 0 1 1 1 1 3 5 9 14 18 19 19

Global LVP - Premium Segments 9 9 9 9 9 9 9 9 10 10 10 10 10 10

ADAS Tier-1 CPV

ADAS - Basic $150 $150 $125 $125 $100 $100 $100 $100 $100 $98 $96 $94 $92 $90

ADAS + Level 2(+) $800 $800 $800 $775 $750 $740 $725 $710 $695 $681 $667 $654 $641 $628

ADAS + Level 3+ (hwy L4) $2,000 $2,000 $2,000 $1,750 $1,600 $1,550 $1,550 $1,550 $1,500 $1,470 $1,441 $1,412 $1,384 $1,356

L4 Features & AV Subs (Stage 1) $6,000 $6,000 $6,000 $6,000 $5,800 $5,700 $5,600 $5,500 $5,300 $5,200 $5,125 $5,000 $4,900 $4,802

ADAS Tier-1 Revenue TAM

ADAS - Basic $5,085 $6,570 $5,075 $5,000 $2,730 $2,330 $2,058 $1,825 $1,600 $1,085 $1,042 $1,000 $961 $942

ADAS + Level 2(+) $800 $800 $8,000 $9,300 $22,500 $25,900 $28,420 $27,275 $26,165 $25,129 $21,117 $17,384 $15,582 $15,271

ADAS + Level 3 $200 $400 $2,000 $875 $3,200 $4,650 $4,557 $4,466 $4,235 $4,068 $3,907 $3,752 $3,603 $3,531

L4 Features & AV Subs (Stage 1) $0 $0 $2,400 $3,000 $4,060 $3,990 $5,488 $15,847 $24,942 $47,963 $69,489 $88,584 $93,584 $91,712

Total TAM $6,085 $7,770 $17,475 $18,175 $32,490 $36,870 $40,523 $49,413 $56,942 $78,244 $95,554 $110,720 $113,730 $111,456

YoY 28% 125% 4% 79% 13% 10% 22% 15% 37% 22% 16% 3% -2%

2019E 2020E 2021E 2022E 2023E 2024E 2025E 2026E 2027E 2028E 2029E 2030E 2031E 2032E

ADAS Basic Content

Camera $45 $45 $44 $43 $42 $41 $40 $39 $39 $38 $37 $36 $36 $35

Radar $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Compute/Software $45 $45 $44 $44 $43 $43 $42 $42 $42 $41 $41 $40 $40 $39

Other $60 $61 $37 $39 $15 $16 $17 $19 $20 $19 $18 $17 $17 $16

Total: $150 $150 $125 $125 $100 $100 $100 $100 $100 $98 $96 $94 $92 $90

ADAS + Level 2(+)

Cameras (2-3x) $135 $134 $131 $128 $126 $123 $121 $118 $116 $114 $111 $109 $107 $105

Radar (3x) $200 $198 $196 $194 $188 $184 $181 $177 $174 $170 $167 $163 $160 $157


DMS $150 $149 $147 $146 $141 $138 $136 $133 $130 $128 $125 $123 $120 $118

Other $40 $48 $56 $40 $36 $40 $39 $38 $36 $36 $35 $34 $34 $33

Total: $800 $800 $800 $775 $750 $740 $725 $710 $695 $681 $667 $654 $641 $628

ADAS + Level 3+ (highway L4)

Cameras (1-5x) $180 $178 $175 $171 $168 $164 $161 $158 $155 $152 $149 $146 $143 $140

Radar (5x) $300 $297 $294 $291 $282 $277 $271 $266 $260 $255 $250 $245 $240 $235

LiDAR (0-1x) $350 $350 $350 $200 $196 $192 $188 $184 $181 $177 $174 $170 $167 $163


DMS $150 $149 $147 $146 $141 $138 $136 $133 $130 $128 $125 $123 $120 $118

Other $370 $383 $397 $312 $201 $179 $206 $233 $210 $205 $201 $197 $193 $189

Total: $2,000 $2,000 $2,000 $1,750 $1,600 $1,550 $1,550 $1,550 $1,500 $1,470 $1,441 $1,412 $1,384 $1,356

AV Subs

Cameras (12x) $513 $503 $493 $483 $474 $464 $455 $446 $437 $428 $420

Radar (8x) $550 $534 $523 $512 $502 $492 $482 $473 $463 $454 $445

LiDAR (3-4x) $1,050 $1,050 $1,050 $998 $948 $900 $855 $812 $772 $733 $697

Compute/Software $2,500 $2,425 $2,377 $2,329 $2,282 $2,237 $2,192 $2,148 $2,105 $2,063 $2,022

DMS $146 $141 $138 $136 $133 $130 $128 $125 $123 $120 $118

Other $1,241 $1,147 $1,119 $1,142 $1,161 $1,077 $1,088 $1,121 $1,100 $1,102 $1,102

Total: $6,000 $5,800 $5,700 $5,600 $5,500 $5,300 $5,200 $5,125 $5,000 $4,900 $4,802

Total

Cameras (12x) $1,679 $2,121 $3,257 $3,594 $5,606 $6,110 $6,512 $7,088 $7,608 $9,229 $10,375 $11,415 $11,520 $11,290

Radar (8x) $230 $257 $2,254 $2,749 $6,585 $7,653 $8,386 $9,019 $9,588 $11,432 $12,362 $13,200 $13,187 $12,923

LiDAR (3x) $35 $70 $350 $625 $1,127 $1,311 $1,531 $3,262 $4,747 $8,379 $11,487 $14,127 $14,439 $13,730

Compute $1,866 $2,352 $5,123 $6,514 $11,866 $13,337 $14,626 $18,359 $21,771 $30,835 $38,291 $45,109 $46,526 $45,600

DMS $165 $178 $1,617 $1,892 $4,617 $5,354 $5,847 $5,870 $5,883 $6,238 $5,991 $5,754 $5,526 $5,416

Other $2,111 $2,792 $2,474 $2,801 $2,689 $3,104 $3,622 $5,816 $7,345 $12,131 $17,048 $21,116 $22,532 $22,497

Total: $6,085 $7,770 $15,075 $18,175 $32,490 $36,870 $40,523 $49,413 $56,942 $78,244 $95,554 $110,720 $113,730 $111,456

Total Sensors $1,944 $2,448 $5,861 $6,968 $13,318 $15,074 $16,429 $19,368 $21,942 $29,040 $34,223 $38,741 $39,146 $37,943


© 2018 Citigroup

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Transforming Mobility As We Know It The Greater Stakeholder Alignment

When thinking about innovations such as AI, connectivity, electrification and big

data, there is perhaps no more obvious use case than the Car of Today. The age of

mass-market personal cars solved many of yesterday’s mobility problems, but also

created new ones, such as congestion, pollution, and underutilized urban

infrastructure. Vehicle safety, while vastly improved, remains a substantial societal

and economic problem which unfortunately is not getting any easier in the age of

distracted driving.

The Car of the Future — which combines advancements in AI, connectivity,

computing power, and electrification — promises not only to address many of these

problems, but also to potentially change personal mobility as we know it. The

immediate question that arises often sounds like this: “that’s nice, but who pays for

it all?” The short answer, as we discuss in more detail later in the report, is that it

can pay for itself, and this creates an historic alignment of stakeholder interests.

This “Great Alignment” can be boiled down as follows:

Societal: The unfortunate reality is there are over 1.3 million annual road

fatalities. In the U.S. we experience ~40k annual fatalities with over 6 million

vehicle crashes, or one crash every ~500k miles driven. Rising global auto

penetration has led to greater road congestion, tailpipe pollution, and

underutilized infrastructure. In Los Angeles, experts suggest there are 3.3

parking spaces for each car. There is also an increasing need to serve an aging

population, those with disabilities, and to ensure better access to personal

mobility across varying income levels. Ultimately, the human and economic toll of

today’s vehicle transportation system serves as the backbone of this alignment of

interests.

New revenue streams: Vehicle data monetization and time-spent-in-car, as

vehicles become more connected with advanced electrical architectures enabling

over-the-air (OTA) updating are new revenue streams being introduced. Those

OTA updates also continuously leverage data and learning iterations to improve

safety throughout a vehicle’s life.

New addressable markets: Urban autonomous RoboTaxi networks that can

provide low-cost, safe and convenient mobility access while offering what we

regard as lucrative financial returns to industry leaders could open new markets.

RoboTaxis could of course also help address urban congestion, pollution, and

infrastructure through less and less vehicle ownership in major cities. There are

also new concepts like AV subscription networks which, as discussed later, could

yield a huge transfer of wealth into new mobility ecosystems, combining the best

of traditional ownership with the benefits of shared mobility. That transfer of

wealth would spur faster adoption (because of greater affordability) thereby more

rapidly transforming the vehicle installed-base into a safer fleet, and eventually,

even a smaller-sized fleet. This, we believe, can be done without compromising a

consumer’s desire to have an instantaneously accessible vehicle 24/7.


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As industry players contemplate these solutions, it quickly becomes evident that the

autonomous-electric-shared themes are somewhat intertwined. For example, an

automaker looking to meet increasingly stringent active safety (or ADAS)

regulations will realize that adding autonomous software features to the already

installed sensors will help recoup costs, particularly once the car is connected and

those features can be delivered over-the-air (similar to Tesla Autopilot). An

automaker launching an EV could be disadvantaged versus one that offers an

EV/AV Subscription, where consumers can be offered a cheaper and more

convenient experience. Similarly, an autonomous vehicle network — whether

RoboTaxi or personally-owned subscription — could be disadvantaged

economically and from a consumer demand perspective if it is not an EV. In all

cases, the car is disadvantaged if it lacks the relevant electrical architecture to

enable vast OTA updates safely and securely.

The AV/EV Tipping Point

The tipping point for all of these exciting trends (electric, shared, connected,

autonomous) should be the entry of the so-called driverless car (AV). Unlike semi-

autonomous vehicles (i.e. level-2, or level-3), a full AV is capable of operating

without a human driver inside the vehicle.

Now, there is no such thing as an all-encompassing driverless car yet, and there

likely won’t be one for some time to come. For the next several years, we expect an

AV to be defined by the specific domain where it can operate fully autonomously, or

what is called level-4. This might be an urban environment on specific routes and on

specific times/weather conditions or it might be a particular radius within a city or

pre-defined routes operating as shuttles or other services.

The key point is that even AVs that are confined to level-4 domains can trigger a

tipping point where new business models (mobility networks) emerge around the

experience. Without level-4, we probably won’t ever to get to level-5. So by the time

we get to level-5, if a company doesn’t already have an established level-4 network,

they risk being left behind. Indeed, this network effect is perhaps the most coveted

asset in the entire Car of the Future race.

So think of level-4 driverless cars as the catalyst for the beginning of a major

industry change and the formation of strategic networks ahead of eventual level-5

models that will further accelerate a broader disruption.


© 2018 Citigroup

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Figure 10. AV Use Case Summary


At the end of this transformation, we think the Auto market will be characterized by:

1. Urban driverless RoboTaxi AV networks (mobility-on-demand, or rideshare,

combined with micro-mobility solutions) operating mainly in urban and some

urban/suburban markets. The race is fundamentally about establishing the

network today. Think of AV as a sort of gradual rideshare 2.0 (RoboTaxi) and

ownership 2.0 (AV Subs) — once these networks are established the eventual

modes of transport could include much more than just AVs—think e-scooters

and aerial vehicles (“flying cars”), which a number of automakers and some

start-ups and aviation companies are already working on. A good example of

this has been respective expansion into micro-mobility by companies like Uber

as part of their broader mobility networks.

2. AV Subscriptions—or ownership 2.0—driverless-capable cars that you

subscribe in order to combine the best attributes of personal ownership with the

benefits of shared AVs. We think this will occur in two stages determined by the

degree of level-4 freedom allotted to the network;

3. At some point, the RoboTaxi and AV Sub distinction will narrow as networks

seek to provide integrated solutions;

4. Traditional ownership in certain vehicle segments and regions (pickups,

commercial vehicles). These traditionally-owned vehicles can still have AV

features sold as standalone options, even if they are “off the network”.

Urban Shared NetworksRideshare networks inclusive of

human-driven, RoboTaxi (gaining

share), micro-mobility & aerial

Vehicles on Road = 44mln

Vehicles ex. Pickups = 39mln

Total RoboTaxi TAM = 6mln

Potential Lost SAAR = 3mln

Suburban Network FormationAV Subs taking share from

ownership. Shared models revolve

around existing rideshare & peer-

to-peer , including through AV Subs

No SAAR impact…

…but automaker share condenses

AV Subs on Road = 59mln

Share of SAAR = 76%

U.S. Autos TodayU.S. Vehicles on Road = 272mln

U.S. Vehicles on Road (ex. pickups) = 230mln

U.S. SAAR = 17mln

Vehicles/Household = 2.2x

Stage 1

2020-2032

Integrated Networks RoboTaxi TAM expands towards level-5. Shared RoboTaxi + AV Sub networks integrate

U.S. RoboTaxi TAM grows to 8 million

Vehicle Density falls to 1.0x per household (126mln vehicles)

Stage 2

2032+


© 2018 Citigroup

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Electric vehicles (EVs) will be a critical competitive input in all three of these mobility

options, since EVs can reduce the cost of ownership while addressing tailpipe

emissions in urban regions (particularly important for the RoboTaxi vertical). We

also believe EVs will be driven by consumer demand for their fun-to-drive and cost-

of-ownership among other attributes.

In terms of timing, we see this occurring through a number of stages:

You Are Here: Today we are seeing two distinct AV development tracks:

1. A handful of companies pursuing various level-4 RoboTaxi AV services to build

urban rideshare networks sometime in the coming one to three years. Most of

these players are focused on major city environments, while a few on very

targeted non-city domains;

2. The continued evolution of autonomous features on personally-owned cars, a

trend that’s partially enabled by active safety (ADAS) regulations and

connected cars. Initially, this evolution will yield level-4 driving features such as

highway-piloting, first at low-speed and then high-speed (think 2020-2022

timeframe). A few years after that, we see a path for personal vehicles to be

sold as AV Subscriptions.

We view RoboTaxis and AV Subs as most powerful in terms of changing personal

mobility.

Figure 11. Four AV Use Cases



© 2018 Citigroup

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At a high-level, we consider the addressable U.S. market for RoboTaxis (at least

initially) in the context of dense urban miles and eventually all urban/close suburban

miles, though in this report we focus more on the urban opportunity since high-

population density is a key enabler of the economics. The remaining addressable

markets (suburbs) will likely remain dominated by household cars for some time to

come, though here too the shift to networked mobility could be felt through the

emergence of AV Subs anchored by sharing platforms.

Phase 1 (2018+) RoboTaxi AV as a Network (urban/suburban): A “RoboTaxi”

can be defined as a fleet of driverless vehicles operating rideshare (taxi) services

within a particular area, mainly cities and surrounding suburbs. We expect

RoboTaxis to begin U.S. commercialization in 2019-2021 led by Waymo, GM-

Cruise, Zoox, Ford, Aptiv (through customer relationships) and leading global

rideshare companies. The race to launch and commercialize RoboTaxis is all

about building a powerful network effect. This network effect is determined by

who can introduce and scale safe, reliable, fast, and low-cost urban RoboTaxi

fleets.

Here is an example: Suppose a RoboTaxi AV fleet launches with greater human-

level safety in a major city. The absence of driver costs allows that AV fleet to

offer a significant price discount to consumers (~40%) versus conventional

rideshare/taxis, while still operating at unit profitability or at least break-even. Let

us also assume the AV is purpose-built with four compartments for passengers to

comfortably/safely share a ride, and cargo space to provide deliveries. The

demand generated by this new AV fleet (initially drawing demand because it is

cheaper) allows the vehicles to: (1) gain further data/driving experience in order

to continuously improve the ride’s safety and speed (more human-like); and (2)

leverage passenger pooling to reduce the per-passenger price/mile, while

gaining learnings on how to deliver the best experience. If we assume this fleet

has a one-year head start versus the next competitor, this lead fleet has an

opportunity to brand itself as safer, faster, and cheaper than its late-arriving

competitor. And if we assume this example occurred in a complex domain (major

city, many routes), then scaling to additional cities might occur faster than had the

fleet started operating somewhere less challenging or less dense. So the fleet

would have an easier time replicating the model in other cities. To that, the AV

RoboTaxi model is expected to commence in urban areas for a few reasons —

urban density allows for respectable unit economics on initially very costly AVs, a

low-speed environment enables a relatively safer deployment, and cities are

ideal grounds to improve upon congestion and pollution challenges. The network

effect described could lead to a ‘few regional winners-take-all’ outcome. All of this

can be thought of as a process that will result in a sort of ‘rideshare 2.0’ network,

where autonomous and other modes of transport (micro-mobility) will evolve in

urban environments.

Phase 2 (2021+) AV Standalone Features (highway first): Around 2020-2021

we expect to see more AV (level 3+) driving features sold as options just like

options are sold today in cars (including through greater use of OTA). Full

highway autonomy will likely prove to be a popular and reasonably affordable

feature — highways tend to be somewhat less complex than urban centers, and

who wouldn’t want to let the car drive while stuck in traffic? Features like this

exist today at a level-2+ and level-3 basis (Nissan ProPilot, GM SuperCruise,

Tesla Autopilot, Audi Traffic Jam Assist), but upgrades to level-4 are expected

around 2020-2021. Frankly, this is the least exciting storyline within AVs because

it doesn’t entail any obvious network effect.

Figure 12. Cruise AV Test Vehicle

Source: GM Media Site (image),Citi Research


© 2018 Citigroup

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Phase 3 (2023+) AV Subscription Networks (Level 4+): The third phase comes

around the early/mid-2020s and entails the potential expansion of AVs into

personally-owned vehicles that consumers can subscribe to, in what we like to

call Ownership 2.0, mostly in suburban/rural domains. Think of this as a hybrid

model that seeks to preserve the value of vehicle ownership (instant undisrupted

access to my car anytime I want with no delay) with the benefits of shared

mobility. As we discuss later, we think a concept like this could become a

powerful step towards establishing profitable networks for eventual migration

above level-4 automation. The biggest gating factor for personal-AVs (relative to

urban RoboTaxis) is AV cost optimization and robust crowdsource mapping, in

our view. A common misconception we often hear about personal AVs is that

they’ll need to first achieve “level-5” before being offered for sale. We don’t view it

that way at all—we see plenty of compelling level-4 applications within

frameworks like AV Subs. For example, in the early-stage the level-4 domain

could be defined as middle-of-the-night with no humans, only at reasonably low

speeds and perhaps initially on specific routes. This effective “level-4+” domain,

in our view, would be sufficient enough to unlock new and powerful ownerships

models. Eventually, the level-4 domain will of course expand, leading to a second

stage where AVs could begin to depress U.S. vehicle density.

Phase 4 (2030+) Integrated Mobility Network: The fourth phase will see some

conversion of various mobility options into integrated mobility networks. For

example, the AV Sub vehicles described above will eventually become less

limited in their driverless domains, pushing the capability somewhat closer to

level-5. At that point, the split between a RoboTaxi and a personally-owned AV

Sub vehicle will become less clear. We believe the most important asset at that

point will be the network itself—ideally one that has both RoboTaxi and AV Sub

capabilities. Once you own the network, then new forms of mobility can be

integrated in — even such as “flying cars” operating on certain routes, or

eventually even personally-owned (subscribed to) on a network. Similar to the

sensor discussion, we think the modes of transport within a network will not be a

one-size fits all, at least not in the foreseeable future. Micro-mobility, AVs and

even aerial vehicles can all serve distinct purposes that maximize their strengths.

It is notable to us that companies like General Motors are not only working on

AVs, but also e-bikes and “flying cars”. We think of the AV network race as the

critical deciding factor for who will lead in the eventual integrated mobility

network.

The following sections will delve into each of these in more detail.


© 2018 Citigroup

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Urban RoboTaxi What Is It?

As mentioned, urban RoboTaxis are autonomous vehicles operating ride-hailing

services in level-4 urban domains. Initially, some of these vehicles might resemble

“cars” as we know them today, but over time we would expect most urban

RoboTaxis to be specifically tailored for the mission — electric, possibly smaller in

nature, bi-directional, different body designs to maximize sensor coverage (a car

designed around its sensing suite), and compartmentalized to maximize

people/things per ride. We expect RoboTaxis to co-exist with human-driven

rideshare/taxis for some time still, but eventually we expect RoboTaxi AVs to gain

substantial share in cities.

Where Are We Now?

Urban RoboTaxis are expected to be the first major deployment theater for

autonomous vehicles. There are a handful of major players preparing to launch

commercial rideshare services within the next few years, all in geo-fenced zones.

As noted above, we view the “race” here as very real based on the notion that the

network effect will result in safer, faster, and lower-cost rideshare networks.

As we see it, there are two major stages in the Urban RoboTaxi race.

Pre-Launch (where the industry is today): The basic to-do list here is as follows:

1. Develop safe, agile, scalable and accountable AV technology, which is of

course the key enabler to entering the market. When it comes to safety, the

goal is to achieve above human-level safety parameters in the chosen domain

(“city XYZ geo-fenced zone, in good weather”). For example, even though it is

estimated a crash occurs once every ~500k miles in the U.S., that number

could be 80-100k miles in a major city. So an AV would need to be developed

to materially beat that local number. But you cannot cut corners (figuratively). If

you overly optimize an AV for safety by compromising that vehicle’s agility, not

only do you risk harming future demand (slow rides) but also possibly causing

accidents by introducing unpredictable road behavior to surrounding human

drivers. This is perhaps the greatest challenge of AV development today.

2. Ideally source a purpose-built AV as the enabler for promoting safe/comfortable

pooled rides.

3. Ideally propel the purpose-built AV RoboTaxi with an electric propulsion system

to minimize urban pollution and better ensure stakeholder acceptance.

4. Develop the infrastructure around the network to maximize robustness (max

uptime, best experience). This would include fleet service (charging,

cleaning/maintaining, parking), rider support via telematics, a remote vehicle

operating center for hopefully rare corner case resolution cases, and

designated pickup/drop-offs spots throughout a city.

5. Lastly, there’s the regulatory element though at the moment this doesn’t appear

to be a major hurdle in the U.S., assuming that all previously mentioned

requirements are met. In fact, in the U.S. we have seen a number of states

become strong proponents of AVs — including California, Florida, Arizona and

Nevada. Though regulations are always subject to change and therefore

require monitoring, our discussions with AV leaders throughout November 2018

suggested no major hurdles.

Figure 13. Ford AV Test Vehicle in Miami

Source: Ford Media Site


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Figure 14. Companies Testing AVs in California

Figure 15. Primary Automated Driving Field Operational Tests

Conducted in Japan

Source: California DMV, Citi Research Source: Strategic Headquarters for the advanced Information and Telecommunications

Network Society, Citi Research Note: Please see Figure 100 for more detail.

Deployment: Once the AV technology and fleet components prove robust,

deployment and commercialization can occur. First let’s define commercialization.

As we see it, there are two main approaches to the urban RoboTaxi domain, both of

them geo-fenced under “level-4” automation. The first is the radius geo-fence, for

example, a particular part of the city with a number of outlets to areas of common

interest like airports or highly-populated suburbs. The second is more of a shuttle

service operating in well-defined routes similar to buses. These would be dense

routes where AVs are more likely to earn a reasonable return on investment. For

example, a ridesharing company with an existing driver network might chose to

launch AVs only in certain routes where those AVs can complement human drivers.

A state like Florida — where ~10% of the population is over 65 years of age and

~117 million people visit each year — is ripe for specific routes or specific

communities being geo-fenced. At the same time, new players looking to establish

their own urban rideshare networks — such as perhaps Waymo, GM-Cruise, Zoox,

Ford and of course rideshare companies themselves — might chose a radius to

maximize service coverage in a radius domain. As always, there is room for

partnerships in the deployment phase.

Post-Launch Scaling: We consider this phase no less important than getting to

launch. This stage would involve the actual scaling of the AV network from city-to-

city in order to establish the network effect touched upon earlier and expanded on

below. AV experts often acknowledge the AV development for City #1 will be very

specific to that city, meaning those vehicles will train heavily on the streets and

simulations to master that particular domain. Mapping is certainly part of it, but the

behavior of the vehicle to that city’s norms and conditions is another important

learning factor. Launching in City #1 is great, but the next question becomes how

quickly a network can expand to Cities 2, 3 and so on. One school of thought is that

those first to launch in City #1 will have a natural advantage to expand into new

cities. But others argue this might not necessarily be the case if the AV software in

City #1 wasn’t designed with scalability in mind. We have heard city-to-city scaling

predictions range from several months to several weeks (post mapping).


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As we start seeing AV networks launch in their initial cities over 2019-2021, an

equally important assessment will be to see how quickly companies can scale to

other cities. There are other key elements for proper scalability: vehicle

manufacturing (we believe EVs are advantaged, so EV manufacturing),

infrastructure support for servicing, telematics, and the ability to leverage prior

learnings/partnerships. On the manufacturing side, we strongly believe the AV itself

must be purpose-built, that is, it cannot simply be a regular car retrofitted to drive

autonomously. A purpose-built AV is arguably safer, more robust (designed for much

longer life, upgradeability), and better suited to maximize load factor, which is a key

component of the network effect discussed in the next section.

Before scaling into a new city, an urban network will first need to ensure that it has

properly scaled within its current launch city. At the onset, RoboTaxi fleets are

expected to face two limitations versus human-driven rideshare/taxis: (1) a geo-

fence zone; and (2) designated pickup/drop-off points as opposed to picking

passengers up from an exact point. This is where existing rideshare networks (such

as the likes of Uber) are arguably advantaged because their networks aren’t

constrained by these two factors, so they could arguably integrate AVs into their

existing rideshare network more seamlessly. We touch on this interesting setup

further below when discussing the industry landscape.

The Network Effect

There are several reasons why some AV players have chosen the urban domain:

1. Cities share a common interest in promoting solutions for urban congestion,

pollution, and greater availability of transportation;

2. Cities offer AV developers a unique combination of a highly complex domain (to

best train software) and low-speed for safety reasons; and

3. For AV companies, the economics appear attractive in dense environments

from day one. The per-mile cost of ridesharing in a highly-dense city today sits

around $2.50-$3.00; we would expect RoboTaxi AVs to commence service

~40% cheaper with positive unit economics on day one. Grabbing share of

rideshare 2.0 is of course paramount to establishing a long-term integrated

mobility network.

The first two network effects — safety and speed — argue that as AVs scale in a

particular city, the AVs can constantly leverage real-world experiences (you don’t

know what you don’t know) to optimize both safety and speed. This would allow

these networks to advertise faster ride times without compromising safety. Naturally

the theory goes that early-movers would be advantaged because their fleets would

become safer and faster than the late-arrivers. Still not everyone agrees with this

theory. Some argue that optimizing safety and speed is not entirely a function of

miles-driven but rather the actual software approach (from perception to decision

making) and simulation of complex scenarios. Others argue the differences in safety

and agility will not be noticeable to most riders, so this supposed advantage is more

theoretical.

The third network effect is arguably most important and less debatable — the load

factor. Assuming all competing RoboTaxi AV fleets are both safe and agile, the

competitive battleground will revolve around price and experience. Clearly the price

of the ride will depend on many factors, but load factor could become a determining

metric. First, to state the obvious, a higher-load factor means you can charge less

per person.

Figure 16. Urban Mobility Cost vs.

Convenience


Mode of Transport Cost/Mile

Taxis $2.50

RoboTax @ Launch $1.50

- RoboTaxi @ 2 People $0.75

Owning a Car $0.76

Mass Transit $0.30

U.S. Cost to Passenger, per mile

Convenience

??

??


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Of course, the challenge is to understand the ideal routing lanes when offering

pooled rides (a data problem that rideshare companies are arguably best positioned

for at the moment) as well as the experience — think of an AV with individual

compartments where people (and things) can share space privately, securely, and

comfortably. Second, pooled rides better address the urban congestion challenges

that RoboTaxi AVs are meant to solve for. This is key to avoiding unintended

consequences — for example if people choose RoboTaxis over public

transportation because AVs are cheaper and more convenient. We have seen

increasing evidence that industry players are favoring purpose-built AVs designed

for maximum load factor. One of those examples came from the Honda-GM-Cruise

partnership in 2018 that included a joint development of a new purpose-built AV.

The second came from Zoox, where management has indicated it is designing its

vehicle for pooled rides.

In a best case scenario, the network effect would see a combination of superior

safety/speed with the lowest cost-per-mile and without sacrificing the experience.

This could achieve a sweet-spot of sorts where RoboTaxis are cost-competitive

versus public transport with arguably higher convenience. For these reasons, most

industry players we speak with view the RoboTaxi AV as either one winner-take-all

or a couple of winners-take-all, by region.

For the foreseeable future (that is, many years), we view the RoboTaxi AV business

as one that will be generally confined to cities where scaling occurs at the local

level. We have previously termed these cities as “Mobility Battlegrounds”. So when

thinking about the addressable RoboTaxi market and the resulting impact on the

industry, we need to drill-down into the county and city level.

Figure 18. Citi Mobility Backgrounds City-Level RoboTaxi Modeling


Figure 17. Waymo Test AV

Source: Waymo Media Site


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Assessing the U.S. Total Addressable Market (TAM) (Citi Mobility Battlegrounds)

Methodology

Given the various approaches to estimating the U.S. TAM, we wanted to provide a

relatively more detailed section of our approach logic, so that the outputs can be

better understood.

With the understanding that a bottoms-up approach would provide a more

comprehensive view of the potential RoboTaxi opportunity, we examined a few

approaches. We began with a few givens: (1) the absolute population had to be

able to support high-utilization of RoboTaxis; (2) the relative population density had

to be high so as to help mitigate non-passenger miles; (3) the markets should have

a relatively higher percentage of people who commute via carpool or public

transportation; (4) land square mile size should be manageable relative to an EV

charge cycle (though this could change with fast charging); and (5) varying

environmental conditions needed to be accounted for.

With this in mind we started to evaluate certain scenarios.

Approach #1 County-Level Data: Our first thought was to look at U.S. county-

level data. This allowed us to effectively evaluate all the criteria above, but we

quickly found out that the sheer size (land square miles) of some counties would

understate the importance of some markets. Additionally, the size of some of

these markets made it more difficult for our EV charge cycle criteria. For

example, Los Angeles County would be understated and tough to create a

manageable EV deployment given its size of ~4,100 square miles and its

population density of ~2,500. So we went back to the drawing board.

Approach #2 Largest City in Each County: We then decided to embark on the

tedious task of grinding through the largest city/town/place in each U.S. county;

although, there are ~3,150 counties in the U.S. We found that even drilling down

one extra layer still understated some markets due to their size in square miles of

the city. While this approach was better, the land square mile size was still not

ideally manageable relative to an EV charge cycle.

Approach #3 Largest Clustered Zip Codes for the Largest City in Each

County: To resolve the size issue, we decided to drill-down one layer further and

look at the most densely populated zip code clusters within each county’s largest

city/town/place, where the size was >100 square miles. What we came up with is

what we believe to be a much more representative picture of the potential U.S.

RoboTaxi TAM. We built deciles around this refined data and layered on top of it

current commuter mobility use cases (driving alone, carpooling, public transport

excl. taxi).

Here’s a graphical example of our approach:


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Figure 19. County > City > Zip Code Cluster Drill-Down – Decile Analysis & Visualization


Figure 20. County Level Drill-Down: LA County

Figure 21. City Level Drill-Down: Los Angeles

Figure 22. Zip Code Clusters Level Drill-Down

Source: Citi Research Source: Citi Research Source: Citi Research


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And here’s a more quantitative example:

Figure 23. Building a New Model: The Thought Process - Examples

State > County > Zip Code Cluster Drill-Down Examples

Population

(# People)

Land Area

(Sq. Miles)

VIO

(# Units)

Sales

(# Units)

1) New York

County Queens 2,358,582 109 914,724 11,568

City Flushing/ Murray Hill 257,031 5 99,684 12,157

City Zip Cluster 257,031 5 99,684 12,157

2) Illinois

County Cook 5,211,263 945 3,644,772 256,912

City Chicago 2,704,958 227 1,891,855 133,353

City Zip Cluster 1,555,426 75 1,087,869 76,682

3) California

County Los Angeles 10,163,507 4,058 7,622,865 659,954

City Los Angeles 3,976,322 469 2,982,333 258,197

City Zip Cluster 1,323,775 68 992,862 85,958

4) Pennsylvania

County Philadelphia 1,580,863 134 962,023 65,863

City Philadelphia 1,580,863 134 962,023 65,863

City Zip Cluster 875,576 47 532,826 36,479

5) California

County San Diego 3,337,685 4,207 2,708,369 178,311

City San Diego 1,406,630 325 1,141,412 75,147

City Zip Cluster 574,449 66 466,137 30,689

Source: Citi Research Note: VIO- Vehicles in Operation

Sorting Through Our Data

Our zip code cluster analysis for the largest cities in each U.S. county suggests that

the vehicle installed-base (# of vehicles on the road, or VIO) at risk for RoboTaxi

disruption stands at ~59 million. Figure 24 shows the building blocks to calculate

this. Still, not all cities and their respective zip code clusters are created equal. As

RoboTaxi economics will scale based on utilization and density, we believe not all

deciles will see RoboTaxi deployment. To that, we believe that the most economic

sense comes from the upper-most decile given its population (allowing for higher

utilization) and density.

In our original Battlegrounds analysis, we used a top-down rule-of-thumb that one

RoboTaxi can replace seven vehicles in operation. For this analysis we went a bit

deeper. As previously noted, we sliced the RoboTaxi market into deciles based on

population size and current commuter mobility use cases. These deciles allow us to

account for, and adjust for, the inequality of the zip code clusters. As the deciles are

built primarily on population size, we need to adjust the aforementioned 1-to-7

RoboTaxi-to-Vehicle-in-Use ratio to account for land square miles. The premise is

simple — if there are less land square miles then you can in theory do more trips,

which means you can remove more VIO;s from the system.


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Figure 24. RoboTaxi Total Addressable Market

United States

Total U.S. Population 325,719,178

Total U.S. Land Area (sq miles) 3,794,083

Total U.S. Population Density 86

Total U.S. Counties 3,141

Total U.S. Vehicle in Operation (VIO) 272,000,000

Total U.S. Full-size Pickups in Operation 42,500,000

Total U.S. Annual Light Vehicle (LV) Sales (2017) 17,100,000

Total U.S. Annual LV Full-Size Pickup Sales (2017) 2,300,000

Largest Zip Code Clusters for Largest City per County

Aggregate Population 88,595,886

% of total U.S. 27%

Aggregate Square Miles of Land 66,417

% of total U.S. 2%

Aggregate Population Density 1,334

Aggregate Vehicles in Operation (VIO) 71,076,869

% of total U.S. 26%

Aggregate Full-size Pickups in Operation 11,782,863

% of total U.S. 28%

Aggregate VIO Exposed to RoboTaxis 59,294,006

% of total U.S. 26%

Aggregate LV Sales 4,307,414

% of total U.S. 25%

Aggregate Annual LV Full-size Pickup Sales 590,231

% of total U.S. 26%

Aggregate Annual LV Sales Exposed to RoboTaxis 3,717,183

% of total U.S. 25%

Aggregate Vehicles in Operation 71,076,869

(-) Aggregate Full-size Pickups in Operation (11,782,863)

(=) Aggregate VIO Exposed to RoboTaxis 59,294,006

(/) RoboTaxi-to-VIO Replacement Ratio 7.8

(=) Required U.S. RoboTaxis 7,576,463


As shown below, we believe the RoboTaxi U.S. TAM — at least in the initial multi-

year expansion phase — stands at ~5.5 million units, with a resulting negative

impact to U.S. light vehicle sales (or SAAR) or ~2.8 million units. Versus our original

modeling from our research years ago, these numbers are somewhat higher. Recall

that our last RoboTaxi model (out to 2030E) estimated a ~3 million unit TAM with a

~1.5-2.0 million unit impact on SAAR.


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Figure 25. RoboTaxi Total Addressable Market

Largest Zip Code Clusters for Largest City per County 90% + Decile

80% Decile

70% Decile

Aggregate Population 88,595,886 59,385,862 12,684,292 6,133,700

% of total U.S. 27% 18% 4% 2%

Aggregate Square Miles of Land 66,417 15,213 12,006 5,409

% of total U.S. 2% 0% 0% 0%

Aggregate Population Density 1,334 3,904 1,056 1,134

Aggregate Vehicles in Operation (VIO) 71,076,869 44,119,579 11,158,518 5,632,770

% of total U.S. 26% 16% 4% 2%

Aggregate Full-size Pickups in Operation 11,782,863 5,560,237 2,105,821 1,264,849

% of total U.S. 28% 13% 5% 3%

Aggregate VIO Exposed to RoboTaxis 59,294,006 38,559,342 9,052,697 4,367,921

% of total U.S. 26% 17% 4% 2%

Aggregate LV Sales 4,307,414 3,060,105 574,267 256,471

% of total U.S. 25% 18% 3% 1%

Aggregate Annual LV Full-size Pickup Sales 590,231 327,876 99,116 53,907

% of total U.S. 26% 14% 4% 2%

Aggregate Annual LV Sales Exposed to RoboTaxis 3,717,183 2,752,229 475,151 202,564

% of total U.S. 25% 19% 3% 1%

Aggregate Vehicles in Operation 71,076,869 44,119,579 11,158,518 5,632,770

(-) Aggregate Full-size Pickups in Operation (11,782,863) (5,560,237) (2,105,821) (1,264,849)

(=) Aggregate VIO Exposed to RoboTaxis 59,294,006 38,559,342 9,052,697 4,367,921

(/) RoboTaxi-to-VIO Replacement Ratio 7.8 7.0 8.5 11.5

(=) Required U.S. RoboTaxis 7,576,463 5,508,477 1,068,124 379,447


Mobility Battleground Spotlight: San Francisco

San Francisco is a key mobility battleground that is seeing a fair amount of

RoboTaxi AV testing from the likes of Cruise, Zoox, Waymo. With Cruise aiming to

deploy a commercial service in 2019 (we presume in San Fran) and Zoox planning

to do the same by year-end 2020, the city will be a closely followed example for this

emerging industry.

There are a number of approaches to modeling mobility outcomes in each urban

battleground. In San Francisco, we opt to consider the number of estimated

vehicles in San Francisco County itself, as well as demand from commuters. The

goal of our simulation is to get a rough sense of the addressable market for

RoboTaxis — how many might the city eventually adopt? What’s the financial

opportunity for that city? What would be the SAAR impact if that city were to

eliminate all non-RoboTaxi vehicles from the road? Which automakers might be

more exposed to that risk? And how does that risk compare to the RoboTaxi

opportunity in the city itself?

San Francisco County has a population of ~871k with ~432k vehicles in operation.

The city also sees a significant amount of daily commuters from surrounding

counties. Under an extreme case, assuming every vehicle on the road is replaced

by a RoboTaxi AV at a ratio of 1:7, the county would need ~62k AVs to service

demand. Taking account of commuters, we believe this would add another ~23k

AVs for a total of 84-85k in total. Eliminating the SAAR in San Francisco County

would yield a ~26k unit headwind. Domestic automakers like General Motors and

Ford are less exposed to San Francisco versus their national market share, so the

negative SAAR impact would be fairly immaterial by our estimation. Major

automakers with a larger position in San Francisco include Toyota and Honda, who

make up roughly one-third of San Francisco County sales.


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Figure 26. Mobility Battleground: San Francisco Heat Map & Share


Going back to the 84-85k assumed RoboTaxis operating in San Francisco (at full

addressable market deployment), this represents a $5.1 billion revenue opportunity

assuming 66k revenue-miles driven, $0.90 per mile of revenue (lower on a per-

passenger basis with higher load factor) and $500/car of annual data-related

monetization. Based on our prior modeling for network margins (discussed more

below), we estimate this would yield an ~$800 million EBIT opportunity.


© 2018 Citigroup

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Figure 27. Mobility Battleground Financial Opportunity: San Francisco Region


The P&L Structure

Revenue drivers include the capacity of the RoboTaxi AV itself in terms of miles

driven, the utilization of those miles driven (revenue-earning-miles) and then the

revenue-per-mile. The revenue-per-mile is a function of competitive factors as well

as the load factor discussed earlier. The other revenue consideration is data

monetization. This can be thought of as monetizing data from the external vehicle

sensors or monetizing the AV ride experience itself. For example, at its November

AV event, Ford showed a concept where riders would be offered a quick stop at a

local store for minimal delay. Some experts believe that this data monetization race

is an equally important part of the network effect discussion. Better data

monetization means you can charge riders less (or even offer rides for free) and

arguably also provide a better experience.

Costs can be thought of in a number of buckets. The first and largest is

depreciation of the AV fleet itself. We believe purpose-built AVs (as opposed to

retrofits) offer many advantages and one of them could a unique design aimed to

extend the life of the vehicle (GM plans to increase useful life by 3-4x). Besides

depreciation, the three other large cost buckets include propulsion, insurance, and


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maintenance. Propulsion costs on an EV would be lower than an internal

combustion engine (ICE)-based vehicle, though the vehicle costs (and depreciation)

could be higher initially. Part of the race in the scaling phase (Stage 2) would be to

bring-down AV cost rapidly mainly via the highest cost components — LiDAR,

compute, and EV-related costs. Maintenance would include costs for replacing tires,

cleaning/parking the vehicle, installing new batteries to extend the vehicle’s life, and

replacing other important components like seats. Outside of fleet-related costs, a

RoboTaxi fleet would need a robust telematics unit for customer and vehicle support

(remote operation if necessary as last resort to a corner case).

Margins we believe should be fairly robust, at least as compared to traditional

Automotive margins. We have previously estimated EBIT margins at scale of 18-

32%.

Figure 28. Building a New Model: The Thought Process: Citi Forecast GM Sample Model Example (But Applicable to Any Urban RoboTaxi Player)


Modeling Inputs 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030GM Share of RoboTaxis 35% 35% 35% 35% 25% 25% 25% 25% 25% 25% 25% 25%

Years of AV Fleet Rollout 5.0

RoboTaxi AV Cost $150,000 $150,000 $150,000 $125,000 $100,000 $75,000 $50,000 $45,000 $40,000 $40,000 $40,000 $40,000

Vehicle Utilization 70% 70% 70% 70% 70% 70% 70% 70% 70% 70% 70% 70%

Annual Data Revenue $10,000 $10,000 $10,000 $10,000 $10,000 $10,000 $10,000 $10,000 $10,000 $10,000 $10,000 $10,000

RoboTaxi EBIT Margin 20.7% 22.2% 19.7% 20.9% 21.8% 30.0% 32.3% 33.7% 33.8% 34.3% 23.3% 19.2%

Revenue per Mile (ex. data)

San Francisco $1.50 $1.50 $1.50 $1.50 $1.50 $1.50 $1.50 $1.50 $1.50 $1.50 $1.50 $1.20

Seattle $1.00 $1.00 $1.00 $1.00 $1.00 $1.00 $1.00 $1.00 $1.00 $1.00 $1.00 $0.80

New York $1.50 $1.50 $1.50 $1.50 $1.50 $1.50 $1.50 $1.50 $1.50 $1.50 $1.50 $1.20

Austin $0.75 $0.75 $0.75 $0.75 $0.75 $0.75 $0.75 $0.75 $0.75 $0.75 $0.60 $0.60

Phoneix Area $0.75 $0.75 $0.75 $0.75 $0.75 $0.75 $0.75 $0.75 $0.75 $0.75 $0.60 $0.60

Others $0.75 $0.75 $0.75 $0.75 $0.75 $0.75 $0.75 $0.75 $0.75 $0.75 $0.60 $0.60

P&L Assumptions

Total Miles Driven 90,000 90,000 90,000 90,000 90,000 90,000 90,000 90,000 90,000 90,000 90,000 90,000

Revenue Miles 63,000 63,000 63,000 63,000 63,000 63,000 63,000 63,000 63,000 63,000 63,000 63,000

Miles/Day 247 247 247 247 247 247 247 247 247 247 247 247

Electricity Cost $0.13 $0.13 $0.13 $0.13 $0.13 $0.13 $0.13 $0.13 $0.13 $0.13 $0.13 $0.13

RoboTaxi Life (in miles) 300,000 300,000 300,000 300,000 300,000 300,000 300,000 300,000 300,000 300,000 300,000 300,000

RoboTaxi Life (in years) 3.33 3.33 3.33 3.33 3.33 3.33 3.33 3.33 3.33 3.33 3.33 3.33

Fixed SG&A Costs per 600k $1,500 $1,500 $1,500 $1,500 $1,500 $1,500 $1,500 $1,500 $1,500 $1,500 $1,500 $1,500

Cost of 60kWh Pack ($/kWh) $150 $125 $100 $100 $90 $85 $80 $75 $75 $75 $75 $75

Monthly Insurance/unit $300 $300 $300 $300 $300 $300 $300 $270 $243 $219 $197 $177

AV Cost $150,000 $150,000 $150,000 $125,000 $100,000 $75,000 $50,000 $45,000 $40,000 $40,000 $40,000 $40,000

Revenue/Vehicle - Industry $104,500 $104,500 $99,450 $90,856 $80,853 $78,436 $68,856 $66,941 $64,108 $64,108 $54,626 $51,555

RoboTaxi Installed Base (TAM)

San Francisco 85,000 85,000 85,000 85,000 85,000 85,000 85,000 85,000 85,000 85,000 85,000 85,000

Seattle 0 0 84,000 84,000 84,000 84,000 84,000 84,000 84,000 84,000 84,000 84,000

New York 0 0 185,000 185,000 185,000 185,000 185,000 185,000 185,000 185,000 185,000 185,000

Austin 0 0 0 67,000 67,000 67,000 67,000 67,000 67,000 67,000 67,000 67,000

Phoneix Area 0 0 0 132,000 132,000 132,000 132,000 132,000 132,000 132,000 132,000 132,000

Others 0 0 0 0 100,000 150,000 700,000 900,000 1,500,000 1,500,000 2,000,000 2,447,000

Total: 85,000 85,000 354,000 553,000 653,000 703,000 1,253,000 1,453,000 2,053,000 2,053,000 2,553,000 3,000,000

RoboTaxi Installed Base (Phased)

San Francisco 17,000 34,000 51,000 68,000 85,000 85,000 85,000 85,000 85,000 85,000 85,000 85,000

Seattle 0 0 16,800 33,600 50,400 67,200 84,000 84,000 84,000 84,000 84,000 84,000

New York 0 0 37,000 74,000 111,000 148,000 185,000 185,000 185,000 185,000 185,000 185,000

Austin 0 0 0 13,400 26,800 40,200 53,600 67,000 67,000 67,000 67,000 67,000

Phoneix Area 0 0 0 26,400 52,800 79,200 105,600 132,000 132,000 132,000 132,000 132,000

Others 0 0 0 0 100,000 150,000 700,000 900,000 1,500,000 1,500,000 2,000,000 2,447,000

Total: 17,000 34,000 104,800 215,400 426,000 569,600 1,213,200 1,453,000 2,053,000 2,053,000 2,553,000 3,000,000


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Key U.S. Players and What to Watch For In 2019-20

First you have the early-movers looking to establish RoboTaxi networks over the

next year or so including Waymo and GM-Cruise. When comparing Waymo and

GM-Cruise on AV tech/development and AV scaling capabilities we think there are a

few comparisons that can be made without much controversy. On the AV tech side,

it’s difficult to precisely compare the capabilities of both networks, but we know

Waymo has been developing AVs the longest, and Waymo is also known for having

designed its own LiDAR sensor which at least one Waymo competitor spoke highly

of in a recent meeting. On the scaling side, GM-Cruise has some advantages in

having access to purpose-built AV/EVs and a robust infrastructure for maintenance

and telematics (OnStar).

The second set of players is the ridesharing companies themselves, including

Uber, Gett, and others. In recent years we have seen rideshare companies

increasingly invest in AV tech while pursuing various partnerships with automakers

and suppliers. Rideshare companies bring a number of key advantages into the

network race—an established customer base, data analytics for load factor

optimization, and well-recognized brands. They are arguably best positioned to

establish a load factor advantage for shared rides, though other companies exist as

well that can offer that data too — one such company is Teralytics, who uses

cellular data to understand movements within a city. The other advantages

rideshare companies have is their human-driver network itself, which gets around

issues like traveling outside of geo-fenced zones, or limiting pickup/drop-offs to pre-

determined locations. Indeed, these are challenges that Waymo and GM-Cruise

would face if they attempted to launch competing networks with the rideshare

companies. This challenge could be solved in two ways: (1) by establishing a small

backup human-driven fleet to serve destinations outside of geo-fenced zones;

and/or (2) establishing partnerships or even codeshare-type relationships with the

rideshare companies. For the rideshare companies, the decision whether or not to

pursue such partnerships would likely rest with their assessment of whether the AV

technology and scaling capabilities of the potentially competing RoboTaxi network

players. How these types of relationships shape up could end up being a major

storyline in 2019.

The third set of players include other companies set on launching rideshare

services. Two that come to mind in the U.S. include Zoox (private company), who is

testing in San Francisco, and Ford, who is testing in Miami and soon Washington

DC. Zoox is expecting to commercially launch by year-end 2020, and is also a

strong believer in the merits of a purpose-built AV/EV. Ford is expected to

commercially launch in 2021, most likely in Miami.

We have been of the view that RoboTaxis are one of a few-winners-take-all market,

though the list of players could change depending on potential future partnerships

and/or codeshare type agreements.

Industry observers often focus on whether a company will launch on time. As

discussed earlier, while launch timing is certainly important, we think there is a bit

too much emphasis on this relative to the bigger picture. Launching and deploying is

a key milestone, but one that will immediately raise an important checklist:

1. How large is the AV fleet itself?

2. The complexity of the AV domain: Is it constrained to a fixed route? Or does

it expand through a large radius? Does it exclude complicated maneuvers like

unprotected left turns?


© 2018 Citigroup

37

3. What are the options for riders to leave geo-fenced zones? Ideally, a

rideshare network would want its app to be used for all travel needs within a

city, as opposed to asking consumers to remember what the geo-fenced zone

served by the RoboTaxis looks like. This is of course an advantage for existing

rideshare networks that can mix AVs with human-driven cars. Failing to address

this issue risks creating the impression that the rideshare network is merely a

novelty or some sort of test run. Remember, once you deploy it’s all about

scaling.

4. The agility of the vehicle: We presume all RoboTaxis deploy safely based on

statistical measures in the real-world and simulation. The distinguishing factor

for riders, however, will likely come from the agility and speed of the vehicle.

The more boring the ride feels the better. Much like the impressive adoption of

micro-mobility (Bird, Lime, Jump), RoboTaxis will face a similar test, and some

of the success ties back to agility/experience.

5. Scaling plans: Does the network spend time ensuring City #1 is done right or

does it immediately start to test elsewhere? And if so, what is the time between

initial deployment and the second deployment?

6. Consumer acceptance: RoboTaxis are expected to deploy with superior

safety parameters versus human drivers. Their safety record should statistically

prove superior to humans if all goes well, but when accidents do occur their

root cause could very well include scenarios that a human would have avoided.

Think about it this way. Where human driving is at its best — handling highly

complex scenarios — is generally where AVs struggle. And where human

driving is at its worst — being distracted or impaired — is where AVs excel.

Whether society tolerates this new reality will be important to monitor.

Figure 29. RoboTaxi AV: Key Players & Pre/Post Launch Assessments


Pre-AV Launch Assessment

Pursuing U.S. Urban RoboTaxi?

L4 Launch Date

L4 Test Fleet Size?

Testing in Actual U.S.

Cities?

AV Headcount Annual Spend

Purpose Built AV?

Is AV an EV? AV Mfg. Integration

Waymo Yes 2018-19 ~800 Yes, Phoenix <1k Unknown Not entirely No 2 OEM Partners

GM-Cruise 2019 (SF) 180 Yes, San Fran ~1.6k ~$1bn Yes Yes GM-Cruise

Zoox Yes ~2020 Unclear San Fran >500 Unknown Yes Yes Building its own

Aptiv Yes. For customers YE'19/early-20 ~100 (~150 YE'18) Yes, Vegas Unknown ~$160mln No No Tier-1 to OEMs

Ford Yes 2021 (Miami) 120 Yes, Miami Unknown ~$500mln Yes No Ford-Argo AI

Tesla Unlikely 2019-2020 Tesla Instl. Base Less Exposure

Unknown Unknown No Yes Tesla

FCA Not Apparent 2021 ~40 (~80 YE'18) Not Apparent - Intel/Mobileye etc. consortium- ---Supplying Waymo--- FCA-Waymo

Daimler Possible (EU 1st?) Early next decade

Unclear Cali in 2019 -- Bosch AV co-develop-- Expected Expected Daimler

VW/Audi Possible (EU 1st?) 2021 (urban) Unclear Not Apparent Unknown Unknown Expected Expected VW/Audi

BMW Possible (EU 1st?) 2021 ~40 (~80 YE'18) Not Apparent - Intel/Mobileye etc. consortium- Expected Expected BMW

Honda Yes (GM-Cruise partner)

Unclear (ex. Cruise)

Unclear Not Apparent Unknown Unknown Yes Yes Honda

Nissan Apparent (Japan 1st?) Early-2020s Unclear Japan Unknown Unknown Expected Expected Nissan

Toyota Not Apparent 2023 Unclear Not Apparent Unknown Unknown Expected Expected Toyota-TRI

Zenuity No 2021 (hwy) 100 Not Apparent >500 Unknown OEM customers


© 2018 Citigroup

38

Figure 30. RoboTaxi AV Post Launch Checklist


Options for Going Outside Geo-fence?

Date of Deployment

Size of Launch Fleet?

Complexity Score (Fixed Route? Radius? Unprotected Left Turns?)

Agility Feedback?

Scaling Plans?

Post RoboTaxi AV Launch Checklist


© 2018 Citigroup

39

The Rise of Micro-Mobility The Urban RoboTaxi AV is by no means the only mobility story enveloping cities

around the world. In recent years we have seen a rapid rise in micro-mobility

solutions such as e-bikes and e-scooters, in additional to traditional non-electric

bikeshare that is station-based. More recently, the rapid expansion of e-scooters

and e-bikes has attracted significant investment from a fundraising perspective,

through M&A and via new entrants.

Micro-mobility generally refers to single occupant modes of transportation such as

bikes and scooters. The market is relatively new and evolving quite rapidly, with

impressive consumer adoption trends occurring in 2018. Deployment of micro-

mobility solutions can be characterized by the mode of transport (bike, e-bike, e-

scooter) and the method of distribution (station-based or dock-less). Bird and Lime

are some of the more well-known e-scooter networks that have launched and

expanded rapidly throughout the U.S.

The addressable market for micro-mobility is potentially very large given that ~50%

of U.S. vehicle trips fall into the 3-5 mile or less category. Clearly, increasing the

penetration of e-bikes/e-scooters expands the range potential for micro-mobility.

The addressable market is tough to gauge but probably includes a mix of public

transportation, taxis, and walking miles (typically 1 mile and under). For this reason,

we don’t view micro-mobility as necessarily a competitor to the vehicle RoboTaxi

market, but rather an expansion of the addressable market for clean/efficient travel,

and an expansion of the network effect itself.

Figure 31. U.S. Miles Driven % Breakout

Source: Highway Transportation Survey

For the consumer, the benefits of using micro-mobility might include:

Greater convenience versus traditional taxis, public transport, or walking;

Lower costs versus traditional taxis; and

The fun aspect of the trip.

0

10

20

30

40

50

<3 mile 3-5 mile >5 mile


© 2018 Citigroup

40

Some of the current industry issues include:

Perceived and actual safety;

Usability in adverse weather conditions;

The current lack of autonomy (so if I want to check my phone or get work done);

Curb space issues; and

Certain legal issues as well as vandalism/theft.

Automakers could conceivably play a role in resolving some of these issues. For

example, automakers could leverage their design/manufacturing base to both

improve safety and unit economics by extending lifespan. Indeed, General Motors

announced in late-2018 that it is developing an e-bike.

The rapid consumer acceptance of micro-mobility in cities globally serves as a

reminder of how quickly mobility networks can rise. Micro-mobility plays into a

similar network effect that we believe exists in RoboTaxi AVs. So we view micro-

mobility as another component of the urban mobility network that is currently being

redefined. Ideally, a network operator would want to offer riders the option for an

urban RoboTaxi or micro-mobility depending on vehicle availability, the length of trip,

complexity of route, weather conditions, and the consumer’s personal preferences.

Figure 32. Person-Miles Trip

Source: Highway Transportation Survey


© 2018 Citigroup

41

China Scooter Focus

Favorable macro condition #1: Scooters provide a cheap and flexible

mobility solution for traffic congestion in urban cities

Public transportation systems in urban cities in China have been under great

pressure with drivers spending over 70 hours per year stuck in traffic, according to

The China Investment Corp (CIC). According to a separate research conducted by

TomTom, 22 out of 50 cities that ranked highest globally according to traffic

congestion levels are in China, further underscoring the severity of the issue (Figure

33).

Scooters offer a cheaper and flexible alternative. The gradual increase in Chinese

disposable income also incentivizes urban households to upgrade their travel tools

(workers and students who commute daily) with minimal investment (compared to

cars or most other modes of private transportation).

High-tech-equipped scooters produced by Niu also solve some of the key concerns

of potential scooter users including:

1. The uncertainty linked to the inability to estimate the remaining travel distance

the battery could support;

2. The difficulty related to manual speed adjustment and the lack of a built-in

navigation system;

3. In the longer-term, uncertainty arising from potential technical issues and

difficulty in identifying the problem or finding a repair solution.

Favorable macro condition #2: Improving road network in rural areas

and increasing rural commuter demand serve as the next sector

growth catalyst

We expect the next leg of “scooterization” to come from rural pockets of China as

increasing commuter demands and better road infrastructure (Figure 34) should

make scooters a preferred mobility option among rural households. A large portion

of the Chinese population resides in high-capacity transit corridors and rural

regions. We note that most of this population is still unable to afford a car which

would allow them to escape their location disadvantage and improve their

accessibility to jobs, goods, and services offered by nearby cities. An expanding

road network and the absence of adequate public facilities in rural areas also

augurs well for personalized transportation demand and e-scooters are a strong

candidate as the next preferred option for rural households to extend their commute

distance within their budgets. We expect demand for e-scooters in lower-tier cities

to be further supported by:

1. Cheap prices of scooters: Niu scooters, which are of in the top end of their

class are still merely equivalent to 5-10% of the operating cost of a car in

China;

2. Relative high speed: Scooters are relatively fast with Niu scooters capable of

speeds up to 70km/h, shortening commute time; and

3. User convenience: Scooters are easy to control and do not require much

energy or skill to operate, which allows commuters or students to extend the

radius of their potential commute (meaning they can live further away from their

workplace or schools).


© 2018 Citigroup

42

Further, the expanding e-commerce and logistics industry in China means that Niu

scooters can double up as utility vehicles and serve as revenue generating tools for

small logistic companies.

Figure 33. Chinese Cities Are Ranked Within Top 50 Cities Globally With Top Traffic Congestion

Source: Tom Tom

Figure 34. Better Road Infrastructure and Increasing Rural Road Network Also Post Opportunities

Source: Chinese Ministry of Transport

66%61%

58%52%

50%49%

47%47%

46%46%

45%45%

44%44%44%

43%43%

42%42%

41%41%41%41%

40%40%40%40%40%

39%39%39%39%39%39%

38%38%38%38%38%38%

37%37%37%

36%36%

35%35%35%35%35%

0% 10% 20% 30% 40% 50% 60% 70%

Mexico City, MexicoBangkok, ThailandJakarta, IndonesiaChongqing, China

Bucharest, RomaniaIstanbul, TurkeyChengdu, China

Rio de Janeiro, BrazilTainan, Taiwan

Beijing, ChinaChangsha, China

Los Angeles, United StatesMoscow, Russia

Guangzhou, ChinaShenzhen, ChinaHangzhou, China

Santiago de Chile, ChileShijiazhuang, China

Buenos Aires, ArgentinaKaohsiung, Taiwan

Saint Petersburg, RussiaShanghai, China

Tianjin, ChinaTaipei, Taiwan

London, United KingdomMarseille, France

Rome, ItalySalvador, Brazil

Sydney, AustraliaSan Francisco, United States

Fuzhou, ChinaShenyang, China

Zhuhai, ChinaVancouver, Canada

Paris, FranceTaichung, TaiwanBrussels, Belgium

Nanjing, ChinaManchester, United Kingdom

Auckland, New ZealandAthens, Greece

Warsaw, PolandRecife, Brazil

Hong Kong , Hong KongChangchun, China

Novosibirsk, RussiaFortaleza, Brazil

Cape Town, South AfricaNew York, United States

Wuhan, China

-

1,000

2,000

3,000

4,000

5,000

6,000

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

Roa

d le

ngth

('0

00km

)

Urban Rural

-

1,000

2,000

3,000

4,000

5,000

6,000

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

Roa

d le

ngth

('0

00km

)

Tiered public roads Non-tiered public roads

0

1000

2000

3000

4000

5000

6000

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

Roa

d le

ngth

('0

00km

)

Highway Non-highway


© 2018 Citigroup

43

Favorable macro condition #3: Lithium ion batteries offer better user

convenience and cost efficiency

Battery costs (battery cell and pack) currently accounts for around 48% of Niu’s cost

of goods sold (COGS) per unit of scooter sold. The latest projection by Gaogong

Industry Research Institute (GGII) suggests that lithium battery prices for scooters

can fall by as much as 12% between 2018 and 2020 to Rmb1.0/watt-hour by

2020E. The decrease in lithium battery price, coupled with policies associated with

the government’s environmental protection initiatives should also incentivize more

consumers to shift from lead-battery-powered e-scooters (which is more common

until now) to lithium-battery-powered e-scooters given the apparent superiority of

lithium batteries over lead-batteries (Figure 37 and Figure 38).

Figure 35. Niu: E-Scooter Unit Cost of Goods Sold Breakdown


Figure 36. Average China Market Prices for NCM Batteries Used to Power E-Scooters

Source: GGI, Citi Research

0%

20%

40%

60%

80%

100%

120%

LithiumBattery Pack

Frame &Other

StructuralComponent

LaborCost

ManufacturingCost

Unit CashCost of

Production

2% 2%

48%

48%

100%

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

1Q14 1Q15 1Q16 1Q17 1Q18 2020 target

Rmb per watt-hour


© 2018 Citigroup

44

Figure 37. Lithium Batteries Are Far Superior to Lead Acid Batteries Across All Aspects.

Lead acid batteries Lithium ion batteries

Energy density 40 Wh/kg 180 Wh/kg

Weight ~28kg ~7kg Preferred under the new 55kg rule

Volume Large (~2x the size of lithium ion batteries) Small Preferred under the new 55kg rule

Charging time 3-6 hours 2-4 hours

Battery life 1-1.5 years 2-4 years

Price Rmb600-1000 for 48V20Ah Rmb1000-1800 for 48V20Ah

Maintenance cost 2-10% initial price Negligible


Figure 38. Transition From Lead-Acid Batteries to Lithium-Ion Batteries


Figure 39. Limited Lithium Battery-Powered Options in the Chinese Market

Source: Company Reports, JD.com

Environmental friendliness Free of hazardous metals such as lead and mercury

Can be easily disposed of and recycled, providing significant

environmental benefits

Favorable government policies Maximum permissible weight of electric bicycles is 55kg, effective

April 2019

Over 95% of existing lead-acid electric two-wheeled vehicles non-

compliant with new weight requirements

0.8

0.7

Lead-acid battery Lithium-ion battery

Cost efficiencyUS$ per 100km

15.9% less 28kg

7kg

Lead-acid battery Lithium-ion battery

75.0% less

User convenienceWeight of 0.96 kWh battery

Niu Yadea Aima

Sunra Luyuan Tailg

N

Series

M

Series

U

Series

25 models in 3 series

Z3S Roman

2 models

Dandan

Phantom Chocolate Bean

1 model2 models

LOK LBE Mini

2 models

1 model


© 2018 Citigroup

45

Figure 40. Lithium Battery Penetration Is Expected to Accelerate in China

Source: Company Reports, Frost & Sullivan

Favorable macro condition #4: Stricter regulations for e-bicylcles

under new national regulatory regime; Double up as an entry barrier

and catalyst for industry consolidation

With the amendment of the General Technical Specifications for Electric Bicycles,

the Chinese government has set a limit on the total permissible weight of electric

bicycles (including the weight of the battery) to 55kg starting from April 2019.

Drivers of electric bicycles do not require a driving permit which makes the product

category more attractive for consumers (such as students and commuting workers).

Since the replacement cycle of electric two-wheeled vehicles is 3-5 years, it is

estimated that most of the two-wheeler vehicles on the road will be compliant by

2022. The CIC estimates this new weight limit would render over 95% of the

existing lead-acid electric two-wheeled vehicles non-compliant.

The new regulation will double up as an entry barrier for many low-tech, low quality

players given that products assembled with lead acid batteries and low quality

components will not be able to reach the 55kg weight limit. We also believe this will

lead to a further consolidation in the motorcycle segment as users would likely be

more selective with their purchases when they need to apply for a driver’s license.

As such, we expect the stricter regulations to eliminate a lot of low-quality players

and push forward the replacement cycle for a significant percentage of scooter

users, especially students and workers who would find the application process of a

driver permit highly inconvenient.

100%98% 98% 97% 97% 96%

87%

65%

59%56%

2% 2% 3% 3% 4%13% 35% 41% 44%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2013 2014 2015 2016 2017 2018E 2019E 2020E 2021E 2022E

China Non-Lithium Battery-Powered e-Scooters China Lithium Battery-Powered e-Scooters


© 2018 Citigroup

46

Figure 41. Electric Bicycles and Motorcycles Regulations Under New Regulations, Effective April 2019

Category Under China National Regulatory

Regime

Definition Weight Speed & Range Limitations Major Brands Key Standards

Electric bicycles Bicycle with an integrated electric lead acid battery or lithium-ion battery

≤55kg ≤25km/h and ~50km

Has a relatively low speed

Domestic: Niu, XDS, Yadea, XDAO

Global: Accell, Amego, Ducati

Must have operating pedals Weight ≤55kg Voltage of battery ≤48V Power ≤400W

Electric motorcycles A plug-in electric vehicle with two wheels powered by lead acid battery or lithium-ion battery

NA >25km/h and ~50km-100km

A driver's license is required

Registration requirements vary in different cities around China

Domestic: Niu, Yadea, Aima, Luyuan, Sunra

Global: Suzuki, Z-electric, KTM, Honda, Energica, Zero Motorcycles, Vmoto

No special requirement for pedals, weight, voltage and power

Manufacturers for e-motorcycles are required to acquire production license


Favorable macro condition #5: Rising demand for premium models of

cheaper goods amid consumption slowdown

The China consumer market is now positioned in a delicate spot where a

consumption upgrade is still underway but the weak economy is dampening interest

for overly priced products. We believe the recent economic trends are creating

market opportunities for premiumization of lower-end goods such as e-scooters.

Figure 42. Niu Is Positioned As a Premium, High-Tech Player

Source: Company Reports


© 2018 Citigroup

47

Figure 43. MSRP Comparison with Chinese Major Peers

Source: Citi Research. Note: Niu and Yadea average selling prices (ASP) are based on blended ASP in company disclosures. Ziaodao, Sunra, Aima, and Luyuan prices are calculated based on a simple average of all of their electric scooters listed on JD.com as of 22 Sept 2018.

Figure 44. Niu: Side-by-Side Specification Comparison for M-Sport with Select Competing Models

Brand Yadea Mina

Aima In MaI

Sunra Apple

Soco CU

Niu M Sport Product

Size of the product (mm) 1675*670*1020 1700*700*1050 1665*707*1020 1782*318*1087 1640*657*1099 Weight 65 kg 65 kg 50kg 60kg 60kg Battery 60V Lead acid 60V Lead acid 48V Lead acid 48 V 18650 series NCM 48 V 18650 series NCM Top speed (km/h) 55 20 20 20 20 Range (km) 60 60 45 80 100 Motor 600W 500W 500W 500W 800W Price Rmb3990 RMB3980 RMB3679 RMB4888 RMB5999

Source: Company Reports, JD.com

1,638

2,056

3,1653,302

3,683

4,112

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

Yadea Xiaodao Aima Suura Luyuan Niu

(RMB/unit)


© 2018 Citigroup

48

Spotlight on Ridesharing in India

City-wide, Not Country-wide, Model

One of the reasons we think car/ passenger vehicle sales will keep growing at a

fairly decent clip is that we view the Uber/Ola model as one which is sustainable

only in the large metros and large cities where the population is high, as is the

population density.

Highly Penetrated in the Metros – Will Growth Slow Down?

According to the company’s websites Uber has been in India for five years and is

present in around 32 cities (as of September 2018) and Ola is present in 110+

cities. It is estimated that while Uber has ~550,000 drivers in India, Ola’s driver base

is much larger at around one million drivers. But that being said, there are

significant overlaps, which imply the total number of app-based taxis in major cities

are around 0.9 – 1.0 million —accounting for one-third of the total taxis registered in

India. There is also a lot of concentration too – it’s estimated that Delhi, Mumbai,

and Begaluru/Mysuru account for around 400,000 app-based taxis.

The Uber/Ola model in its current form is mostly suited for office related commutes,

where commuters want viable alternatives to over-crowded local trains and are

unhappy with the conditions of local taxis. This is in our view the ‘creamy layer’ of

the revenue of public taxis and accounts for probably 50% or more of the daily

revenue of a local taxi on a weekday. If one assumes that a taxi does 15 trips a day,

around 6-10 trips would be medium/long haul (10-20 km) and account for a majority

of the revenues of a taxi driver. The reasons why the cab aggregators are

successful vis-à-vis the local taxi operators are: (1) non-monetary — better

product/service, cleaner vehicles versus public cabs; and (2) monetary — the

aggregators charge less/incremental kilometer than the public taxis.

Even with all those advantages, ride sharing is seeing a slowdown in growth, as the

penetration in key metros like Mumbai and Delhi is quite high. Based on reports in

the press, the pace of growth has slowed from 90% in 2016 to 20% year-to-date.

Figure 45. Average Rides/Day on Ride Sharing Platforms (Uber and Ola)

Source: Timesnow.com, Citi Research

In October 2018, drivers of Ola and Uber went on a long 10+ day strike protesting

against fare declines and an increase in vehicles, which resulted in monthly

incentives halving from earlier levels of around Rs80k-Rs100k per month.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

2015 2016 2017 2018 YTD

(mn)

Our autos team believes that the ride

sharing model is sustainable only in large

metros/cities where population is high and

so is the population density

Current ride sharing model suited for long-

haul office commute – where the initial fare

is 2x of local public taxi, but cost per

incremental km is ~ ½ of local cab


© 2018 Citigroup

49

AV Subscriptions AV Subs Distinction from RoboTaxis

The case for urban RoboTaxis and broader car/ride sharing often cite that today’s

cars sit idle >90% of the time. There is of course a lot of truth to that statement and

much of the urban challenge is about freeing up congestion and infrastructure in

highly-populated cities.

But we don’t think the percentage of a car’s idle time is a blanket metric that can be

applied across regions, because that statistic could mean very different things

depending on the circumstance. For example, in a densely populated region with

good weather, forgoing vehicle ownership in favor of sharing can make a lot of

sense — lower mobility costs with minimal, if any, impact to convenience.

However, in a rural suburb with poor weather, forgoing ownership is a tougher cost-

benefit equation. In those regions one could argue that the car isn’t really “idle”

because it provides the consumer with the peace of mind of knowing s/he can

instantly access mobility at any time, entirely at their option. That peace of mind is

worth something. At the same time, we don’t subscribe to the view that consumers

have lost interest in cars as an aspirational product or as an object of desire.

Perhaps the best example of this today is Tesla, whose impressive product

momentum actually ties back to classical automotive selling points — a highly-

styled vehicle that’s considered really fun to drive with high technological content. If

EVs are going to resurrect a certain love of driving (as Tesla is arguably doing), then

abandoning the business of “selling” cars doesn’t go away, but rather morphs into a

different type of ownership model that leverages the best of what AVs and EVs have

to offer with zero compromises, as we believe the concept of AV Subs does.

So the concept of AV Subscriptions (AV Subs) in the suburbs attempts to preserve

the value of instant-car-access (“ownership”) with a shared platform that would

allow each market to eventually strike its desired balance of shared/owned vehicles.

In doing so, AV Subs would aim to unlock substantial value while tapping into parts

of the automotive value chain that sit outside of automakers’ reach today.

What makes AV Subs compelling, in our view, are two factors that we think tend to

be overlooked: (1) we don’t need “level-5” automation to achieve a compelling

stage-1 AV Sub model; and (2) there’s a self-funding element here that makes the

business case, even in what we call stage-1, compelling. We’ll get into all of this

below, but these are the initial points to keep in mind.

AV Subs—How Might it Work?

Stage 1

First, let’s define the vehicle as an EV (not mandatory but advantageous) that’s AV

capable under certain domains (level-4). Unlike RoboTaxis, whose level-4 domains

would mostly surround complex urban environments, the level-4 domains for AV

Subs (Stage 1) would be fairly easier domains from technical, financial, and

practical perspectives. In this Stage 1, we envision two level-4 domains for AV Sub

vehicles:

1. Highway-driving, a feature that many are working on for the 2021 timeframe

(the not so exciting part of AVs);


© 2018 Citigroup

50

2. Driverless operation under the following conditions—no humans in the car, and

only in the hours of 10pm-5am, and only under acceptable weather conditions,

and only under pre-determined point-to-point routes such as my house-to-my

dealer. Why? Because we believe this is a sweet-spot of sorts where AV can

add meaningful value to the user, establish a network for the mobility operator,

and strike a balance between AV safety, agility, and scalability.

Second, let’s define what an AV Subscription is in the eyes of the consumer. For the

consumer, an AV Subscription is sort of like a lease — you pay a monthly fee for

24hr/day access to your vehicle for XYZ months or years. It’s a personal vehicle for

all intents and purposes. There’s no obligation to share your vehicle and you can

access it anytime — just like today.

What’s different in an AV Sub is that:

The monthly fee includes the entire cost-of-ownership, so the “lease” payment is

inclusive of propulsion, insurance, and maintenance/repair;

As a subscriber, you get to enjoy both the AV features (highway level-4

autonomy, level-2+/level-3 in other domains) as well as new level-4 network

features that arise from the driverless mode being enabled in the middle of the

night with no humans, as was explained above. Of course, you also get the

benefit of safety from the level-4 system operating all the time while you drive

(when a human is in the car, it must be driven). Before we talk about the

numbers, let’s answer the question you might be asking by now: what sort of

value proposition does a no-human/middle-of-the-night driverless car bring to the

table? Let’s answer this in the eyes of the consumer and then talk about

numbers:

– Vehicle Servicing: Under the AV Sub agreement, all vehicle servicing would

be done in the middle of the night at a dealer — from mandatory work like

tires/repair to optional services such as car washes. For the consumer, this

would be a convenience offering allowing you to unlock time normally spent

repairing and maintaining your vehicle. Interestingly, some tire companies like

Goodyear Tire are experimenting with new retail models (Roll, by Goodyear)

where consumers have the option of having the tire replacement vans come to

them. AV subs could look to offer a similarly hassle-free servicing model for all

of the vehicle’s required plus optional appointments.

– Vehicle Swapping/Renting: Under the AV Sub agreement, consumers would

have the option to either swap their vehicles for another vehicle in the network,

or simply rent out a vehicle by ordering one to arrive in the middle of the night.

To ensure constant availability of vehicles, the network (OEM) would always

have a small fleet of extra vehicles available at dealer lots — an assortment of

leisure and utility vehicles that might fit a consumer’s occasional need/want.

Perhaps a pickup truck for occasional utility, a sports car for fun, a larger

vehicle for a family trip. According to peer-to-peer car-share firm Turo, two of

the five most popular vehicles on its platform are the Wrangler and Mustang,

suggesting a value proposition exists in granting consumers (easy) access to

what could be considered more specialty vehicles. Swapping would of course

be optional and positioned as another convenience feature for allowing

consumers to access different vehicles than the one you have. But this backup

fleet (initially used for swapping) would eventually be used as a RoboTaxi fleet

(in “Stage 2”, as discussed below) during commuting hours, whereas in non-

commuting hours the AV Sub vehicles would be sourced (at the consumer

option) for rideshare demand, which brings us to the next point…


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– Peer-to-Peer Sharing: In addition to swapping, subscribers could — entirely

at their option — leverage the platform for peer-to-peer sharing. This is the

concept of your car making money for you when you are not using it, though in

Stage 1 this would be a bit constrained to your car needing to depart and

return in the middle of the night. Those renting your car would enjoy highway

AV features, certain level-2+/level-3 features plus added safety and EV

benefits, but they’d be driving the car since it’s only driverless capable in the

middle-of-the-night. So the AV initially doesn’t operate as a RoboTaxi, but

rather an advanced car share vehicle that travels from your house to a peer-

to-peer lot (via a dealer, as discussed below). You can share all the time, or

never. But the option to make money on your car is always there, and we think

that’s a nice option to have even if you don’t intend to share your car with the

network. For the network, the opportunity here is sizable. Consider that the

U.S. car rental market is a ~$28 billion annual business. Also consider that

Turo has seen the number of cars on its network rise from 66k in 2015 to 231k

as of May 2018. Peer-to-peer might not be for everyone or ideal at all points of

the subscription period, but there’s little question that a real market does exist

as evidenced by the success of current peer-to-peer platforms, as well as

newer market entries such as GM’s Maven division.

– Home Deliveries: Subscribers could have their AVs pick up orders either at

stores or distribution centers that are partnered with the network. Again, this

would leverage specific routes with dealers being used as hubs, as we’ll

discuss a bit later. The value-add here is that consumers would save on

delivery fees and enjoy extra convenience of perhaps faster deliveries. This

too would be marketed as a convenience service and money saver.

The AV Sub network would own the vehicle throughout its life, perhaps with a FinCo

partner(s). Subscribers could be tiered depending on the age of the vehicles, with a

different pricing structure for each tier. Here’s a graphical example of the structure

using Ford as an example:

Figure 46. AV Sub – Basic Flow Diagram (Ford Example)



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We have described what the service offering of an AV Sub might look like. Now let’s

get into the cost of the subscription to the consumer. Cleary, the AV features and

services described above offer some value-add. But the power of AV Subs, in our

view, will come from the potential for the monthly subscription cost to more or less

equal the consumer’s prior cost-of-ownership for an ICE vehicle.

Here’s why we think this can happen in about five years:

1. First, consider that an automaker and finance company today only get

about half of the lifetime revenue a car generates. The other half or so goes

to insurance companies, fueling companies, and maintenance/repair

companies—some of which are high margin businesses that see >30% gross

margins. We believe that an AV Sub model could allow networks/OEMs to

recapture this other half of the pie by effectively bringing these economics in-

house. That would allow AV Sub providers to price AV Subs compellingly (with

improving returns) in order to generate demand that would build a broader

network (for Stage 2, discussed a bit later) and of course gain share on

automakers incapable or too slow to catch up. Think of it in a similar context as

the RoboTaxi network race described earlier.

Figure 47. Estimated Lifetime Revenue Economics of a Car


2. AVs can unlock two parts of the untapped half of the pie: The first unlock

comes from lower insurance premiums owing to a level-4 sensing suite that

would likely be considerably safer than today’s forward-facing ADAS. Indeed,

companies like Aptiv continuously report synergies between their AV teams and

their traditional ADAS teams. The second unlock comes from the driverless

domain itself (no human in the middle of the night). What this does is allow the

network to effectively steer all maintenance to its dealer network and

incorporate into the monthly payment all maintenance and other services that

the consumer would have otherwise incurred outside of that automaker’s

ecosystem.

OEM/FinCos53%

Fuel Providers21%

Insurance Cos.15%

Repair/Maint.11%


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Given the relatively higher profit margins earned in the automotive aftermarket

space, we think the math can work for both the AV Sub network (higher

margins) and the subscriber (same monthly payment, greater convenience).

Subscribers would still have service choices where it makes sense — for

example fitting the vehicle with a particular tire brand — but the network would

effectively take a larger share of the value chain.

3. EVs unlock the third part of the untapped half of the pie. As EVs become

more affordable relative to ICE, the benefits from lower electric propulsion costs

(vs. ICE) plus lower maintenance costs become more pronounced in the overall

consumer proposition. The AV Sub network could accrue the EV’s lower cost-

of-ownership (electric propulsion and arguably maintenance) and pass along

some savings as part of the monthly subscription fee. This is why EV capability

is advantageous in this model, even if it’s technically not mandatory at the

onset. And because the AV Sub network would offer optional peer-to-peer

revenue for subscribers, the prospects of earning income on your car could

increase demand for longer-range EV options. Since EV batteries are known to

degrade over time and lose substantial range under extreme weather

conditions, the option of peer-to-peer sharing could provide consumers with an

added confidence boost to purchase longer-range EV variants.

Figure 48. AV Subscriber vs. Conventional Ownership

Figure 49. AV Subscriber: Drivers of Monthly Subscription Cost Unlock

Source: Citi Research Source: Citi Research

Let’s run some illustrative examples:

Figure 50 below illustrates an estimated cost-of-ownership in the life of a $35k ICE

vehicle. While there are several ways to illustrate this, we assumed a vehicle goes

through three owners during a 15 year lifecycle. We also assumed 15k miles

driven/year, $2.50 gas price and 23mpg real-world driving for the vehicle. The

resulting outputs show the cost of lifetime ownership based on third-party

maintenance data. Maintenance service follows the owner’s manual for the

respective example model.

Today’s Car AV Sub

AV Sub Reaching Similar Cost of

Ownership as Today’s Car

AV Safety Lower Insurance

AV Network

Bringing Repair/Maintenance In-

House

Peer-to-Peer Sharing = Car Makes Money for

You (optional)

EVLower Propulsion +

Maintenance


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Figure 50. Illustrative Internal Combustion Engine Lifetime Cost of Ownership (Cash Flow)

Owner 1 Owner 2 Owner 3

ICE 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Lease $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Finance $6,589 $6,589 $6,589 $6,589 $6,589 $3,630 $3,630 $3,630 $3,630 $3,630 $1,738 $1,738 $1,738 $1,738 $1,738

Fuel $1,630 $1,630 $1,630 $1,630 $1,630 $1,630 $1,630 $1,630 $1,630 $1,630 $1,630 $1,630 $1,630 $1,630 $1,630

Insurance $896 $927 $960 $993 $1,028 $1,355 $1,355 $1,355 $1,355 $1,355 $1,208 $1,208 $1,208 $1,208 $1,208

Maintain $158 $236 $342 $1,266 $661 $236 $1,760 $158 $342 $1,163 $1,055 $158 $342 $1,654 $661

Repair $0 $0 $112 $266 $388 $100 $100 $100 $100 $100 $100 $100 $100 $100 $100

Other $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Total $9,274 $9,383 $9,634 $10,745 $10,297 $6,951 $8,475 $6,873 $7,057 $7,878 $5,732 $4,835 $5,019 $6,331 $5,338

Monthly $773 $782 $803 $895 $858 $579 $706 $573 $588 $657 $478 $403 $418 $528 $445

Source: Edmunds, Company Reports, Citi Research

The monthly payments above (what consumers pay today for an ICE on a total-

cost-of-ownership basis) can be thought of as the AV Network’s revenue ceiling,

meaning that to spur rapid adoption and that all-important network effect, the AV

Subscription should ideally cost subscribers no more than owning a conventional

car. Now, let’s estimate the cost to operate the AV Sub network itself. Here’s how we

have roughly modeled it:

We assumed the EV/AV vehicle comes at a $6k added variable cost versus the

conventional car — again we are talking about 2023-2025+ so by then the

industry will benefit from lower-cost sensors (LiDAR), lower cost and more

efficient computers, learnings from AV developments (including RoboTaxi

players), and next-generation cameras and radars (higher resolution/range). We

view this as reasonable based on supplier commentary around future level-4+

costs.

The network, in this case an automaker, sells the vehicle to a FinCo and leases

the vehicle back. We impute the leasing cost of the vehicle over the 15-year life

at a $0 salvage value using an interest rate of 4.5% and a price for the vehicle of

$41k which takes the $35k price imputed above and adds $6k of AV content.

EV range at 300 miles on a 70kWh battery at $0.12 electricity cost.

Insurance savings of 40% vs. a conventional vehicle thanks to the AV sensor

suite performing highly-advanced ADAS at all times.

Maintenance costs savings of 35% due to lack of aftermarket mark-ups and

presumably lower lifetime maintenance cost of an EV. In year-9 we assume that

the network replaces the EV battery.

This results in a rough P&L estimate for the AV Sub (are shown in Figure 51):


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Figure 51. Illustrated AV Subscription Network (Cash Flow)

AV/EV Sub 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Lease $3,865 $3,865 $3,865 $3,865 $3,865 $3,865 $3,865 $3,865 $3,865 $3,865 $3,865 $3,865 $3,865 $3,865 $3,865

Finance $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Fuel $420 $420 $420 $420 $420 $420 $420 $420 $420 $420 $420 $420 $420 $420 $420

Insurance $538 $556 $576 $596 $617 $813 $813 $813 $813 $813 $725 $725 $725 $725 $725

Maintain $47 $98 $98 $767 $98 $98 $818 $47 $6,398 $493 $561 $47 $98 $818 $98

Repair $0 $0 $73 $173 $252 $65 $65 $65 $65 $65 $65 $65 $65 $65 $65

Other $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Total: $4,870 $4,939 $5,031 $5,821 $5,252 $5,261 $5,981 $5,210 $11,561 $5,656 $5,636 $5,122 $5,172 $5,893 $5,172

Monthly $406 $412 $419 $485 $438 $438.39 $498 $434 $963 $471 $470 $427 $431 $491 $431

Network 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Revenue (ICE cost) $773 $782 $803 $895 $858 $579 $706 $573 $588 $657 $478 $403 $418 $528 $445

COGS ($406) ($412) ($419) ($485) ($438) ($438) ($498) ($434) ($963) ($471) ($470) ($427) ($431) ($491) ($431)

Gross Prof $367 $370 $384 $410 $420 $141 $208 $139 ($375) $185 $8 ($24) ($13) $37 $14

Annual $4,404 $4,444 $4,602 $4,924 $5,045 $1,691 $2,495 $1,664 ($4,503) $2,223 $96 ($287) ($154) $438 $165


As shown, the illustration above suggests the network could operate profitably by

charging the same monthly cost as a conventional car with all the added AV

convenience, cost (parking), and revenue sharing optionality benefits.

Of course one other cost to consider is the backup swapping fleet. Assuming an 8%

ratio of excess cars (so 8k backup fleet for every 100k vehicles in the network), we

can calculate the network P&L inclusive of this cost. To be conservative, we haven’t

assumed any revenue generation from these vehicles, meaning that the network

wouldn’t rent these AVs out while idle. As shown, even with this cost we think a

100k unit network could generate close to $2.7 billion of lifetime gross profit

under this model.

Figure 52. Auto Subscription Network Variable Profit Illustration (Cash Flow)

Fleet 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Revenue $927 $938 $963 $1,074 $1,030 $695 $848 $687 $706 $788 $573 $483 $502 $633 $534

COGS ($487) ($494) ($503) ($582) ($525) ($526) ($598) ($521) ($1,156) ($566) ($564) ($512) ($517) ($589) ($517)

Gross Prof $440 $444 $460 $492 $504 $169 $249 $166 ($450) $222 $10 ($29) ($15) $44 $17

Fleet Cost ($39.0) ($39) ($39) ($39) ($39) ($39) ($39) ($39) ($39) ($39) ($39) ($39) ($39) ($39) ($39)

+ Data Monetization $42 $42 $42 $42 $42 $42 $42 $42 $42 $42 $42 $42 $42 $42 $42

Adj. Gross Profit $443 $447 $463 $495 $508 $172 $253 $169 ($447) $225 $13 ($26) ($12) $47 $20


We have covered the basic economic model and the proposition of an AV Sub from

the consumer vantage point. Before proceeding to Stage 2, it’s important to discuss

why the middle-of-the-night, human-less and route-to-route AV model is doable and

ideal in this Stage 1, and how it might work practically:

Over the past year we have learned two things about AV software development:

– Route-to-route development tends to be somewhat easier than developing for

a larger radius around a city. This is something that top AV executives have

publically acknowledged, and is confirmed by our own experiences such as

riding in Aptiv’s AV fleet in Las Vegas between casinos. You can map better,

keep track of changing road conditions better, and train your vehicles better on

a route. That’s not to say the AV would fail outside of the designated routes,

but that the routes (often two-three between destinations) would allow for a

more straightforward development mission.


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– In the RoboTaxi race, safety of course comes first but it cannot come at the

expense of reasonable human-like agility. This is a major issue in the

RoboTaxi development race right now. Drive too slow and nobody will want to

use your service, plus risk upsetting other motorists or worse, causing

accidents. An AV Sub (Stage 1) model would shortcut these challenges — it

would be route-to-route and, by operating at night, would allow for more

conservative agility. If my AV Sub takes 5 minutes longer to return home at

3am, I really don’t care. If a remote operator is required to resolve a corner

case and the car had to pull over, I also don’t care. Plus there’s nobody sitting

in the back upset that they’re late to their destination. And of course if

accidents do happen, the risk of human injury/fatality is diminished by the

absence of a human in the car and fewer vulnerable road users in the middle

of the night.

The route-to-route domain could leverage auto dealers, who would presumably

end up offering many of the AV Sub services themselves. This would allow the

automaker to cover a large geographic area with pre-mapped routes using

dealers as level-4 hubs. Even when an AV Subscriber uses peer-to-peer or home

delivery services, the AV would “connect” through the dealer. For example: My

House-to-Dealer-to-Peer-to-Peer Lot (airport)—Back to Dealer—Back to My

House. Similarly, My House-Dealer-Mall-Back to Dealer-Back to My House. In

the years prior to offering AV Subs, automakers could pre-map these known

major routes into major target towns. So think of city center at XYZ town being

mapped to the nearest few dealers using 2-3 routes. Then, when a consumer

who lives in that town subscribes to the AV, the vehicle could spend the first few

weeks calibrating the last mile from that city center to the subscriber’s home.

That could be accomplished in a number of ways. For example upon delivery of

the AV Sub, either the consumer or dealer would drive from the town center to

the consumer’s home through two-three routes, several times. This calibration

phase would need to occur prior to the AV Sub services being enabled through

OTA. Based on our discussions with AV experts, we don’t think this would be a

major technical challenge in around five years from now.


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Figure 53. Ford: Southern California Dealer Distribution (Dealers = Possible Level-4 AV Hubs)


Stage 2

Establishing a customer base in Stage 1 would set up an AV Sub provider well for

Stage 2, when AV capabilities presumably expand beyond the nighttime domain

noted above. Let’s discuss the implications of this domain expansion.

Going back to the city center-to-my house scenario route, think of an AV that can

operate without a driver on that route pretty much at any time. Now you introduce

the car dropping you off in town and picking you up — saving on parking fees

and time. We have to imagine that the AV learnings obtained in Stage 1 would

enable entry into Stage 2 somewhat faster — even if a company relies on

simulation for AV development, the domain experience would likely still provide

valuable data and added confidence to deploy more widely. So this demonstrates

the importance of establishing one’s AV Sub network early.

Think of the AV being able to operate as a suburban RoboTaxi (we like to refer to

it as RoboTaxi “light”) throughout the day. This could be done in an expanded

level-4 domain. For example, people who work in town XYZ could lend their cars

from 9am-4pm while they’re at work. Clearly, this doesn’t address rush hour

commute demand but does address mobility demand that, today, is perhaps

served by an excess household car in the suburbs. Demand during commuting

hours could then be serviced, at least to some extent, by the backup AV fleet

that’s normally used for swapping. This would aim to maximize the utility of the

AV Sub vehicles (share when you are not using, at your option of course) and the

backup fleet (offer for swaps/rentals to AV Subscribers, use as RoboTaxi “light”

when available). Doing this would of course further expand the peer-to-peer

revenue TAM available to the subscriber and network. If the AV Sub network also

happens to be in the urban RoboTaxi business by then, synergies could emerge

to integrate those networks.


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Stage 2 is where household vehicle density could start declining even in suburban

markets, though as discussed below, not every region is created equal. Multiple car

households in the suburbs could start to rely more on available AVs (sourced from

peer-to-peer networks) for miles that were previously dedicated to car #2 or car #3.

The network providers who led Stage 1 would have some clear advantages into

Stage 2. First, getting to Stage 2 from a technical perspective is difficult enough, so

gaining learnings of individual towns/counties could allow a faster step up to Stage

2. Second, to the extent the network already had some peer-to-peer capability,

brand recognition could go a long way in Stage 2. For example, consumers (the AV

Subscribers, not the users of the peer-to-peer sharing) attracted to the idea of

“making money using their cars” would likely favor a more liquid network that has

been around and perhaps also has an urban RoboTaxi network. And because peer-

to-peer is a newer concept that often raises immediate questions (what if they return

my car dirty?), brand familiarity can go a long way — similar to popular sharing

networks today. If Stage 2 takes off in terms of supply/demand for peer-to-peer or

lending out one’s car to a RoboTaxi network, having an established network

(RoboTaxi, AV Sub, or both) would likely become a significant competitive

advantage.

Assessing the Addressable Market

Stage 1 is relatively straightforward because the biggest change would likely occur

at the automaker/network provider market share level. Because Stage 1 would

unlikely lead to declines in personal vehicle ownership, the addressable market can

be defined as the total number of U.S. vehicles on the road, less those vehicles

presumed to be impacted by the urban RoboTaxi expansion described above. Said

differently, all vehicles not located in the more urban (higher population density)

regions where RoboTaxi services could, in theory, begin to migrate towards AV

Subs or “ownership 2.0”.

Another way to look at it would be to assess lease penetration as a proxy for AV

Sub demand — since leasing is the closest model today to what an AV Sub would

be (though of course with huge differences and new services). Recall that in our

RoboTaxi analysis we concluded that the SAAR would be at risk to fall by about ~3

million units (mostly urban domains), taking the “normalized” SAAR to ~14 million.

Assuming AV Subs capture ~50% penetration (somewhat higher than today’s ~30%

lease penetration to account for more attractive service), that would suggest annual

subscription sales of ~7 million vehicles eventually forming a total installed-base of

~117 million. Full penetration would amount to ~233 million vehicles.

Stage 2 is perhaps more interesting to consider even if it’s still many years away. In

Stage 2 the lines between ridesharing and owning/subscribing start to blur, so

household vehicle density could decline even in the suburbs. Of course, like

RoboTaxis every market is different. To narrow down counties that might be more

suited for vehicle density to decline, we filtered our U.S. County data as follows:

1. The first filter of our data was to look at the market post the RoboTaxi

transformation. As such, we removed any county where the largest city cluster

represented the entirety of the county.

2. The next filter was to make sure we only looked at the remaining counties that

are overweight those who drive their own vehicle to work alone. The higher

concentration of people who drive their own vehicle to work alone also gives us

a sense of underutilized vehicle density which could potentially come out of the

system with a subscriber-based service.


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3. Lastly, we filtered our data on county size in square miles and population

density. While we would prefer the county size to be relatively small in order to

allow for less travel time and distance between peer-to-peer and hub AV

actions, we also left in counties that were large yet had a high population

density.

After applying all these filters, we were left with ~400 counties that we believe were

most logical for the Stage 2 AV Sub model. The total TAM was as follows:

– Population: 71 million (of ~326 million total U.S.)

– Total VIO: 64 million (of ~200 million total U.S., excl. RoboTaxi exposure)

– VIO/Household: 2.26 (vs. 1.98 U.S. Average)

– % of People Commuting by Car Alone: 82% (vs. 76% U.S. Average)

Where Are We Now?

Unlike RoboTaxis, it is still very early days for this concept we refer to as “AV Subs”.

That’s not to say the concept isn’t being discussed at major industry players —

indeed in November 2018 Ford management told us that such a concept had been

discussed internally. Also, we view the Tesla Network concept as being something

similar to an AV Sub, though as discussed later, we see some issues with Tesla’s AV

approach to date. Some of the other hints we have seen from automakers include

experimenting with subscription-based services for non-AV cars today, and pursuing

peer-to-peer sharing models such as GM’s Maven division.

Still, we are frankly surprised that automakers don’t appear to be pursuing AV Subs

with the same aggression as we are seeing within urban RoboTaxis — particularly if

one believes that RoboTaxis are one of a few-winner-takes-all outcomes. For

automakers, AV Subs plays on three key competitive advantages versus traditional

tech companies.

1. First, there is the dealer network itself playing the role of mission-critical level-4

hubs. Real estate is an advantage in AV development, and large car companies

have more of it than both small automakers and traditional tech companies.

2. Second, AV cost optimization is a major enabler of making the math work for

AV Subs. Whereas the RoboTaxi race is a sort of “brute force” approach to

deploy/scale networks first while figuring out cost optimization later, AV Subs

would need to be reasonably optimized on day one. This is exactly what

automakers and their Tier-1 partners are really good at.

3. Third, AV Subs are a great way to differentiate an automaker’s future EV

offering. Tesla’s product success to-date is clearly forcing automakers to

benchmark their plans to Tesla’s capabilities and product appeal. In our view,

launching EVs alone isn’t enough. If there is any weak spot in Tesla’s tech

approach, we think it’s with AV development. Later in the report we go into a

case study on this. If our assessment is correct, then automakers have a real

opportunity to “one up” Tesla by leveraging their capabilities for AV networks.

We are not talking about merely launching a “better” automated driving feature

than Autopilot—by all accounts GM did that with SuperCruise. We are talking

about a mobility re-defining moment like AV Subs where new services/features

can be delivered at the same monthly cost of ownership as before. If

automakers want to “beat” Tesla, they need to take the mobility experience to


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another level as opposed to merely launching products that are numerically

competitive with Tesla’s.

Figure 54. Past and Current Vehicle Subscription Programs

Brand Volvo Ford/Lincoln Cadillac Hyundai Porsche BMW Mercedes-Benz

Sub Service Care by Volvo

Canvas BOOK by Cadillac Ioniq UNLIMITED+ Porsche Passport Access by BMW Mercedes-Benz

Collection

Vehicles

New Volvo XC 40

Pre-owned 2015MY and 2017MY Ford/Lincoln Fleet

ATS-V, CTS-V, CT6, XT5, Escalade

Hyundai Ioniq Launch Plan:

Cayman, Boxster, Macan, Cayenne

Accelerate Plan:

911 Carrera, Cayman, Boxster, Panamera,

Macan, Cayenne

Legend Plan:

X5, 4 Series, 5 Series

M Plan:

M4, M5, M6, X5M, X6M TBA

Term Duration 24 Months 1-12 Months Month-to-Month 36 Months Month-to-Month Month-to-Month TBA

Mileage

15,000/year Packages:

1) 500/month

2) 850/ month

3)1,250/month

4) Unlimited

2,000/month Unlimited Unlimited Unlimited

TBA

Cost

Trim Based:

$600-$700/ month

Package Based:

1) $395/month* (Lowest Configuration)

2) $1,695/month** (Highest Configuration)

$,1800/month Trim Based:

$275-$365/month

(+$2,500 due at signing)

Vehicle Based:

1)Launch: $2,000/month

2)Accelerate: $3,000/month

(+$500 activation fee)

Vehicle Based:

1)Legend Plan: $2,000/month

2)M Plan: $3,700/month

(+$575 activation fee)

TBA

Vehicle Exchanges

No Unlimited: $99/swap 18 within 12 months No Yes, no limitations Yes, no limitations TBA

Where Today U.S. West Los Angeles & San

Francisco NYC, Dallas, Los

Angeles Los Angeles Metro Atlanta Metro Soon to Nashville Soon to

Nashville, Philadelphia

Additional Info

Upgrade to a new Volvo in as little as 12

months

*$395/month: 2015 Ford Fiesta SE with <500

miles/month for 12 month term; **$1,695: 2017 Lincoln

Navigator Reserve w/ unlimited miles for 1 month

term

Insurance not included

Coming this June



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Figure 55. U.S. Dealership Network Share


GM24%

Ford17%

FCA14%

Toyota9%

Honda8%

Nissan7%

Hyundai5%

KIA4%

VW4%

Subaru3%

Mercedes2%

BMW2%

Mazda1%


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It All Started with ADAS…. Before we delve into an Autos 2030 analysis combining the potential future impact

of RoboTaxis and AV Subs, it’s worth spending some time to discuss ADAS,

because we view ADAS as something of a foundation from which automakers might

begin to consider AV Sub models in the retail auto channel.

The term Active Safety — or ADAS — refers to technologies that can proactively

minimize or eliminate vehicle accidents by preventatively braking, steering, or

simply warning a driver of imminent danger. Much like successful technologies

before it, ADAS has been strongly endorsed by major global regulators, with the EU

NCAP (New Car Assessment Program) leading the way. In the U.S. twenty

automakers who collectively represent >99% of the U.S. auto market have

committed to making automatic emergency braking (AEB) standard on all cars no

later than the National Highway Traffic Safety Administration’s (NHTSA’s) 2022

reporting year — or effectively no later than model year 2023 vehicles. China has

generally followed the European NCAP programs.

The drivers here are three-fold: (1) increasing regulatory demand; (2) consumer

demand for safer, more convenient cars; and (3) new compelling business models

such as AV Subs, which in Stage 1 do not require anything remotely close to level-5

to provide value.

Where AVs and ADAS first start to intersect is in what we have previously described

as the ADAS-to-level-2+ virtuous loop — a prior thesis of ours that appears to be

playing out. The simple premise is that as ADAS regulations become more

stringent, it actually encourages automakers to embrace higher levels of autonomy

by leveraging the increasingly advanced sensing/compute being deployed for

ADAS. We think a similar outcome could play out in the next decade with next-

generation ADAS requirements feeding into AV Subs.

Regulation

The U.S. alone experiences ~6 million vehicle crashes per year claiming ~40k lives

and over 2 million injuries. The vast majority of crashes are thought to be caused by

human error; it is estimated that 93% of U.S. accidents are caused by human error,

with a similar ratio in Europe. Alcohol remains a major issue in the U.S., a

contributing factor in ~30% of fatal crashes. Speeding is also a major factor (at

~30%), driver distraction (~20%), lane keeping (~14%). and failure to yield (~11%).

It is estimated that if a driver is afforded an extra ½ second of response time,

roughly 60% of accidents could be avoided or mitigated. The cost of U.S. traffic

accidents exceeds ~$900 billion per year.

Globally, traffic fatalities totaled 1.3 million in 2017 — the World Health Organization

has set a target to cut the number of traffic fatalities by 50% by 2030 — with an

estimated >50 million people seriously injured and >$3 trillion of costs from road

crashes. Though passive safety technologies (airbags, seatbelts) have vastly

improved vehicle safety in recent decades, they have arguably reached their limits,

particularly in the current age of increased distracted driving.

Figure 56. U.S. Crash Statistics

Source: NHTSA, IIHS, Company Reports, Citi Research

U.S. Crashes per Year 5.5mn

% Human Error 93%

Fatal Crashes per Year 32,367

% Involving Alcohol 31%

% Involving Speeding 30%

% Involving Distraction 21%

% Involving Lane Keeping 14%

% Involving Yielding 11%

% Involving Wet Road 11%

% Involving Fatigue 3%

% Involving Erratic Operation 9%

% Involving Inexperience Issues 8%


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Figure 57. EU NCAP Adoption Timeline

2018E 2019E 2020E 2021E 2022E 2023E 2024E 2025E

AEB Cyclist

Driver Monitoring

AEB Pedestrian – Back-over

AEB- Junction/Crossing

AEB

Head-on

AES (steering)

V2X

Source: NHTSC, Euro NCAP, JNCAP, KNCAP, Global NCAP, C-CNCAP, Citi Research

Figure 58. AEB Penetration vs. Other Technologies that Achieved Near Full Penetration


0%

25%

50%

75%

100%

2015 2016 2017 2018 2019 2020 2021 2022 2023

AEB at ESC RampAEB at Rear Camera RampAEB at Side Curtain Airbag RampAEB at Side Airbag Ramp

Some ADAS abbreviations:

ACA = Adaptive Cruise Assist

ACC = Adaptive Cruise Control

AEB = Advanced Emergency Braking

BSD = Blind Spot Detection

DMS = Driver Monitoring System

ESC = Electronic Stability Control

FCW = Forward Collision Warning

LDW = Lane Departure Warning

LKA = Lane Keeping Assist

SAS = Steering Angle Sensor

TJA = Traffic Jam Assist

VRU = Vulnerable Road Users


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ADAS 1.0 and the Rise of Level-2+

In recent years (2014-18), most ADAS regulations have focused on automatic

emergency braking (AEB) and to a lesser extent, lane departure warning. Within

AEB, the main focus has to date focused on detecting vehicles in front of the host

vehicle, as well as pedestrians either standing in a vehicle’s path or crossing a

street. What ADAS 1.0 did not mandate was detecting cars at a wider field-of-view

(i.e. getting cut off, or while turning), or detecting a vehicle at any angle. ADAS

testing also didn’t traditionally mandate all weather and lighting conditions. Said

differently, ADAS 1.0 mandated some of the most pressing and solvable vehicle

safety challenges, but it was just a first step. Today, ADAS 1.0 safety features

appear well on their way to achieving full-penetration. We think “full” ADAS

penetration by 2025 (roughly two-thirds of global vehicle volume) has become the

consensus view.

Yet, as ADAS gradually becomes standard issue, automakers have and will

continue to face a common profitability dilemma of selecting what content/features

to slot into the vehicle in order to replace previously lucrative ADAS profits when

ADAS was offered as an option (which is still prevalent today). As previously noted,

these considerations have and should continue enticing automakers to leverage

onboard sensors to go the “extra mile” and upgrade basic ADAS features into level-

2+ semi-autonomous systems. And as cars become more connected to the point

where the ADAS software can be updated over-the-air (OTA), the push-up from

basic ADAS towards level-2+ will only accelerate.

Migrating from “basic ADAS” to a “level-2+” does require some additional content.

This would include more robust sensing coverage in the front of the vehicle (to

detect vehicles cutting-into lanes), mapping capabilities (with update capability),

driver monitoring, and more advanced compute/integration. On the software side,

level-2+ also requires better lane/free space/road boundary detection versus basic

ADAS, as well as stronger general object detection, traffic light detection, and

overall sensor fusion for longer range. Level-2+ also requires robust human-

machine-interface (HMI) for driver interaction, situational awareness, and monitoring

(Driver Monitoring Systems or DMS).

Aptiv estimates the incremental content opportunity from migrating to level 2+ from

basic ADAS could be $500-675. While not cheap, this “extra mile” seems

reasonable for mid/high vehicle trim levels (i.e. on the Chevy Silverado/Sierra

pickup trucks, we est. mid/high trim levels = ~80% of total volume). In the coming

years we see two new potential drivers that could further entice automakers to

increasingly move to level-2+ on mid/high trims.

Figure 59. Global Auto Fatality Stats


United States 15

Germany 7

Japan 7

South Korea 26

China 36

India 315

Thailand 119

Brazil 71

Fatalities/ 1,000 Vehicles


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Figure 60. Current Vehicle Subscription Programs

Vehicle •Hands-Off Duration Geo Fenced

Traffic Jam Assist

Speed Constrained Highway Only?

Driving Monitoring System (DMS)?

BMW X5

Driving Assistant

Professional

•TJA = "extended hands-off time"

•~ 4 sec when using ACC w/ LKA outside of TJA

No Yes TJA < 37 mph No Yes

(Camera Based)

Cadillac CT6

Super Cruise

•Indefinite

•Disengages if DMS detects driver is not paying attention

Yes (Specific

Highways)

No < 85 mph Yes Yes

(Infrared Based)

Nissan Rogue

ProPILOT Assist

•Hands-on

•~10 sec before warning

No No • < 90 mph

• Steering assist operates at ≥ 60 km/h (37 mph)

No No

Audi A8

Adaptive Cruise Assist

•TJA = hands-off

•ACC = hands-on

No Yes �•ACA = 0-250 kph (155.3 mph)

• TJA < 37.3 mph

No Yes

(Camera Based)

Mercedes-Benz S- Class

Drive Pilot

•~15 seconds before warning No No • Active Distance Assist: < 130 mph

• LKA operates between 37-124 mph

No No

Volvo XC90

Pilot Assist

•Hands-on No No < 80 mph (steering system deactivates at speeds >87 mph)

No No


The first is connectivity. As mentioned, automakers are increasingly installing

embedded modems into the car to enable over-the-air software (OTA) updates, big

data monetization, and consumer services. As connectivity attach-rates continue to

climb in the years ahead, automakers will increasingly have the ability to sell level-

2+ convenience features (i.e. software) on the same hardware already performing

ADAS functions (similar to Tesla today). This means the delivery of vehicle option

no longer occurs solely at the time of purchase, but throughout a car’s entire life —

a new revenue stream for automakers and suppliers. We view this as a powerful

enabler for automakers to earn a profit on ADAS content and/or improve customer

loyalty. This, in our view, enhances the decision to add $500-675 of incremental

cost to enable a level-2+ system.

The second is the potential for future insurance discounts for consumers to account

for greater safety than level-2 systems can provide (vs. ADAS). Equipping vehicles

with more sensors naturally expands the safety of a vehicle. And additional safety

raises the future prospects of insurance discounts. A modest insurance discount

could go a long way towards funding the cost of ADAS — perhaps even funding all

of it. We believe that a 15%-30% discount to a customer’s insurance premium is

theoretically reasonable, using current plug-in aftermarket solutions as a proxy.

Such discounts could fund most if not the entire cost of a semi-autonomous (level-

2+) content package.

In our view, by early/middle of the next decade, level-2+ features will likely become

the sweet spot onboarding choice for mass market vehicles in the mid/high trim-

levels.

Figure 61. Content Per Vehicle Estimates by Various Autonomy Levels

Content Per Vehicle L 0-1 L2 L2+ L3 L4+

Aptiv $300 $475 $975 $4,200 -

Veoneer $300 $650 - $1,750 $7,000

Magna $500 $1,200 - $3,400 $4,500



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ADAS 2.0 and the Rise of AV Subs

Just as ADAS 1.0 created a financial and strategic incentive for automakers to

pursue level-2 and level-2+ features, the continued regulatory demand for greater

ADAS sophistication could create a secondary push towards level-3 and then AV

Subs.

Again led by the EU NCAP, regulatory demands for ADAS are expected to become

more advanced from 2020 onwards both from a sensing coverage perspective (i.e.

intersections requiring a wider field-of-view, driver monitoring systems) and software

demands—such as detecting a vehicle, cyclists, and motorbikes from every angle,

including oncoming. Much of this is expected to begin implementation in the 2020-

2022 timeframe. As RoboTaxis start deploying around the same time, we can only

imagine that regulatory bodies will demand that some of their safety-related features

make their way into all cars.

Automakers are once again facing a future that will require superior sensor

coverage (particularly on the sides of the vehicle) and increasingly demanding

software. As they contemplate these demands, by 2020-2022 they should also have

greater access to advanced mapping data (both HD and crowdsourced) that is

critical to enabling autonomous driving. So whereas RoboTaxis can be described as

a brute-force approach to building networks now, AV Subs have an evolutionary

element that can be partially thought of as a natural extension of the trend toward

more and more ADAS features plus the unlocking of automated driving.

Consumer Demand

Besides regulation, demand for increasing automation will come from consumers

themselves both from a safety and convenience perspective. Indeed, many

automakers are already leveraging ADAS technology for advertising campaigns.

Safety often ranks amongst the top 10 considerations for vehicle purchase, and we

believe consumers are gradually becoming more aware of ADAS as a key

component of that. Indeed, our proprietary AutoTech Tracker dataset has generally

shown favorable U.S. ADAS penetration trends throughout 2018 (on six tracked

high-volume vehicles) despite macro headwinds such as rising interest rates.


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Figure 62. Citi AutoTech Tracker LIVE! Dataset – A Look at Forward-Collision Alert Penetration Rates


Business Model

The classic Automotive business model challenge is how you price for new

technologies, and when those technologies are beneficial for society, how do you

balance increased penetration with profit objectives? As technology evolves through

greater sensing/compute/mapping and OTA capabilities, this problem will only

become more pronounced.

Where we see a tipping point is in the ability for AV Sub business models to

effectively fund the cost of level-4 through the capability of the subscription itself to

increase the size of the available profit pool. This could lead to a virtuous cycle

which, in our view, could rapidly increase AV Sub adoption and therefore achieve a

safer vehicle installed base. We see a few steps in this cycle:

Deploying AV Subs drives consumer demand for the network features (swap,

service, peer-to-peer, delivery services), convenience driving features (level-4

highway, level-2+/level-4 everywhere else), and safety (far superior ADAS

features on level-4 sensing/compute suite).

Per our thesis, we think AV Networks can do this profitably by leveraging the

lifetime vehicle revenue which currently sits outside of the automaker ecosystem.

In addition, the data leveraging opportunity should be greater on these vehicles.


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Deploying more AV subs creates two network effects. First, it builds a robust

network for peer-to-peer sharing. In other words, those consumers interested in

renting their cars out might look more favorably at established networks as a

means of earning money. Second the AVs themselves would gain real-world

learnings towards eventually pushing up to increasingly level-5 scenarios.

Figure 63. AV Sub Network Migration Over Time


Deploy More AV Subs (L4)

Drive Consumer Demand

(Safety + Convenience)

Increase Profit TAM

Build Network Effect for Liquid

Sharing

Build Network Effect for Better AVs (Eventually

L5)


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The Auto Industry 2030+ Today, the ADAS market (from a Tier-1 supplier perspective) is sized at roughly $5-6

billion. The consensus view is that growth will remain strong but that adoption of

level-4 will be slow and gradual. This might prove true for the next two to four years,

but our thesis around the potential for AV Subs, for example, implies the potential

for an adoption tipping point perhaps in either the early or middle part of the next

decade.

If we are right, a few things will happen:

Today’s $5-6 billion ADAS market size (from a Tier-1 supplier perspective) could

reach ~$111 billion by 2030E, which we view as above consensus. Magna, for

example, sees an $80-95 billion market in 2030. We believe the AV ramp could

prove much faster in the 2023 to 2030 timeframe, thanks to the value unlock of

new network business models. Our actual addressable market size doesn’t

appear to differ too far from those of many Tier-1 suppliers, but we just believe

the ramp could occur faster.

For the automakers/network providers, the lifetime addressable profit pool of the

car would likely rise significantly versus today’s industry. This includes the

simulated impact of lower global auto sales by 2032E (which is debatable since

declines in developing economies could be offset by emerging/frontier

economies, which have very low auto penetration today yet large populations),

because we view the AV/EV network-related profit opportunity to be larger.

It’s not all good news, however. The nature of the network effect will likely leave

fewer automakers participating in this larger-sized market. Automakers who are

late or unable to execute on AVs, are behind on EVs, and/or fail to build sharable

platforms might end up being left behind. To be sure, the value of selling exciting

and desirable cars won’t change — but those lagging on AVs could lose share by

having less competitive offerings.

From an automaker perspective, this could result in a handful of laggards and a

few very large winners who would benefit both from the increased market size as

well as higher market share. That said, unlike RoboTaxis, where we see a few

regional winners taking all, we suspect there would be a handful of automakers

who could positively participate in an AV Sub network.

From a supplier perspective, the growing pie may not necessarily benefit all

exposed companies the same, since the sheer complexity of developing AVs

likely won’t afford automakers the luxury of spreading out contracts over as many

suppliers as they typically would like. Recall too that suppliers are less directly

exposed to the RoboTaxi vertical (lower volume), but are instead exposed to the

>$100 billion ADAS market we forecast by 2030 driven by models like AV Subs.

That >$100 billion estimated addressable market could in theory become

available to a handful of AV supplier leaders.

Now we’ll get into the above numbers and our simulations in a bit more detail.

First, the analysis — which is U.S. focused — aims to bring together previously

discussed AV Network concepts/simulations for both urban RoboTaxis and both

stages of AV Subs. Since we are looking at addressable markets and consequent

industry impacts, the analysis is meant to err on the aggressive side though within

mathematical and practical reason. So think of this as an “all goes well” analysis but

not some utopia exercise — as mentioned in prior sections we have delved into

data at the county level (for all U.S. counties).


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Lastly, the projection forecast is out to ~2032 based on our best estimates on

relevant inputs like AV costs, but we wouldn’t get too caught up debating whether

XYZ outcome is necessarily 2032 or a few years before/after.

With that, we have grouped the analysis into three major shifts occurring through

two time periods:

2019-2032

Urban RoboTaxi impact in the 90%-decile of U.S. counties analyzed earlier (see

page 28)

Stage-1 AV Subs taking share at a level-4 domain.

2032+

An expansion of RoboTaxi to the remaining markets analyzed earlier, plus level-5

AV Subs that effectively integrate mobility networks to the point of significantly

reducing personal vehicle density.

Let’s go through our analysis with more detail:

2019-2032

Today the U.S. automotive market consists of 272 million light vehicles on the road.

An automaker today might expect to earn $10k of variable profit/unit over the life of

these vehicles — so a $2.7 trillion lifetime opportunity. On an annual basis with ~17

million units sold, the addressable market is equal to ~$172 billion in variable profit.

Here’s how our 2019-2032 simulations would affect that number:

First, we simulate the urban RoboTaxi impact in the 90%-decile of U.S.

counties (used in our analysis earlier): The most desirable RoboTaxi markets

(from a population density perspective) cover 39 million vehicles on the road (ex.

6 million pickup trucks which we don’t believe would be materially affected, if at

all). So out of 272 million U.S. light vehicles on the road, we assume that 39

million vehicles are displaced by 6 million RoboTaxis — using a 1:7 ratio we have

used in prior analyses based on past academic studies. The reduction of tens of

millions of vehicles from U.S. roads would reduce annual U.S. auto sales to ~14

million units — meaning the lifetime addressable market for automakers goes to

$2.3 trillion from $2.7 trillion. Importantly, we believe the pickup truck market

would be largely unaffected, if at all, so automakers exposed to that market

wouldn’t be hurt by the decline in U.S. auto sales. Rather, automakers selling

sedans in the affected urban counties would likely be most affected. As for the 6

million RoboTaxis on the road, based on our modeling above we estimate they

would generate lifetime profit of $170 billion for a total addressable market of

$2.5 trillion, with annual profit at $47 billion assuming a conservative 4-year

lifecycle. The $170 billion RoboTaxi market would likely be split across a few

regional winners.

Second, we simulate Stage-1 AV Subs taking share of what’s left of U.S.

auto sales after the RoboTaxi impact. Because of the inherent level-4

limitations of Stage 1, no vehicle density changes are likely to occur. The biggest

impacts of this phase occur from potential automaker market share shifts

(leaders in AV Subs take share) and an expansion of the profit TAM from the re-

definition of the auto supply chain. In our simulation we assume that 76% of U.S.

auto sales are “sold” as AV subscriptions by 2032E, with AV Subs accounting for

25% of the U.S. installed base by then. The U.S. market would therefore look like

this:


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The post-RoboTaxi U.S. SAAR of 14 million units would be split into 11 million

sold under AV Subs and 3 million under a normal own/lease model. That 3 million

would have an addressable lifetime profit market of $1.7 trillion. The RoboTaxi

market is unaffected by this so the lifetime profit market remains at $170 billion.

The AV Sub market at that time — or 59 million units on the road — would offer a

lifetime profit TAM alone estimated at ~$1.7 trillion based on prior modeling. So

the total addressable “auto” market (lifetime) rises to $3.6 trillion mainly by

transferring some of the economics that today sit outside the automaker/FinCo

ecosystem (maintenance/repair, insurance, propulsion) into the AV Sub

ecosystem.

At this point, we are still in level-4 operation, both for urban RoboTaxis and AV

Subs. Now let’s simulate a post 2032+ scenario.

Figure 64. Simulating U.S. Mobility Changes (Today Through RoboTaxis & AV Sub Stage 1) – in Millions


2032+

An expansion of RoboTaxi to the remaining markets analyzed above, plus

level-5 AV Subs that effectively integrate mobility networks to the point of

significantly reducing personal vehicle density. This scenario sees

integrated mobility networks where all cars are effectively sharable in that AV

Subs can serve as RoboTaxis outside of cities while purpose-built RoboTaxi is

still handle cities because they’re better designed to carry multiple occupants or

packages. We assume that U.S. vehicle density declines to 1.0x per household,

resulting in the loss of >100 million vehicles from the road. The demand for miles

no longer served by these vehicles is captured by an increase of ~2 million

RoboTaxis (from 6 million to 8 million, effectively the entire addressable market

analyzed earlier) and AV Subs being shared when not in use. For the sake of

discussion and conservatism, we assume that AV Sub revenue is captured by the

consumer as opposed to the network itself, so we haven’t raised the lifetime

addressable profits of an AV Sub network. Under this simulation, the total

addressable market rises to $3.8 trillion comprised of $235 billion of RoboTaxis

and $3.5 trillion from AV Subs (lifetime opportunity).

Market State Post Impact Market State Pre & Post Impact Market State Pre & Post Impact

U.S. Vehicle Population (VIO) 272 U.S. Vehicle Population (VIO) 233 Total U.S. Vehicle Installed-Base 233

U.S. Full-Size Pickup Population 43 U.S. Full-Size Pickup Population 43 U.S. AV Subs Installed Base 59

U.S. VIO excluding Pickups 230 U.S. VIO excluding Pickups 191 Non-AV Subs Installed Base 174

U.S. Urban RoboTaxis 0 U.S. Urban RoboTaxi Installed Base 6 U.S. Urban RoboTaxi Installed Base 6

U.S. Light Vehicle Sales (SAAR) 17 What's Impacted? U.S. Light Vehicle Sales (SAAR) 14 What's Impacted? U.S. Light Vehicle Sales (SAAR) 14

U.S. Households 126 U.S. Households 126 AV Subs (SAAR) 11

U.S. Drivers 223 U.S. Population 59 U.S. Drivers 223 AV Subs Take Share of SAAR U.S. Households 126

U.S. People Population 326 Land Sq. Miles 15 U.S. People Population 326 - % of SAAR (2032E) 76% U.S. Drivers 223

U.S. Population Density 86 Vehicles on Road 44 U.S. Population Density 86 - AV Subs on Road 59 U.S. People Population 326

Vehicles/Driver 1.2x - Pickups on Road 6 Vehicles/Driver 1.0x % of VIO 25% U.S. Population Density 86

Vehicles/Household 2.2x Remaining Vehicles 39 Vehicles/Household 1.9x Vehicles/Driver 1.0x

- Stage 1 Subs don't reduce density Vehicles/Household 1.9x

RoboTaxis Introduced 6 - But could condense OEM share

Addressable Market (Lifetime of Car) Lost Vehicles in Road (39) Addressable Market (Lifetime of Car) -Mainly in suburban regions where Addressable Market (Lifetime of Car)

1. Auto 1.0 TAM Lost SAAR (3) 1. Auto 1.0 TAM RoboTAxis not initially ideal at L4 1. Auto 1.0 TAM

Variable Profit @ Sale $8,500 Variable Profit @ Sale $8,500 Variable Profit @ Sale $8,500

Aftermarket (0-3yrs) $1,500 Aftermarket (0-3yrs) $1,500 Aftermarket (0-3yrs) $1,500

Total Variable Profit $10,000 Total Variable Profit $10,000 Total Variable Profit $10,000

Auto 1.0 TAM $2,720,000 Auto 1.0 TAM $2,334,400 Auto 1.0 TAM $1,744,400

2. Urban RoboTaxi AV TAM 2. Urban RoboTaxi AV TAM

RoboTaxi Lifetime Revenue $946,544 RoboTaxi Lifetime Revenue $946,544

RoboTaxi AV Profit TAM (18%) $170,378 RoboTaxi AV Profit TAM (18%) $170,378

3. AV Subs TAM

AV Subs Lifetime Variable Profit $1,652,000

Total: $2,720,000 Total: $2,504,778 Total: $3,566,778

Addressable Market (Annual) Addressable Market (Annual) Addressable Market (Annual)

Auto 1.0 TAM $172,000 Auto 1.0 TAM $144,477 Auto 1.0 TAM $34,477

+ Urban RoboTaxi TAM $47,391 + Urban RoboTaxi TAM $47,391

+ AV Subs TAM $110,133

Total: $172,000 Total: $191,868 Total: $192,001

Urban RoboTaxi:

(2019-Early 2030s)

AV Subs Stage 1 (2023-Early 2030s)

Today's U.S. Auto Market Post Urban Robotaxis Post Stage 1 AV Subs


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Figure 65. Simulating Mobility Changes (Stage 1 AV Subs Through Stage 2)- in Millions


Mobility End Game: Integrated Networks

When it comes to various forms of mobility, we don’t necessarily foresee a one-size-

fits-all mode of personal transport. Depending on one’s location, the car’s particular

use case, one’s desire for instant mobility, or privacy, different mobility solutions can

make sense from e-scooters/bikes, RoboTaxis and eventually flying cars operating

specific routes. And given that people’s tastes, moods, needs, and circumstances

can change quickly, consumers are likely to prefer a mobility solution that’s all

encompassing — again competing on price, convenience and experience. The race

we are starting to witness is about establishing networks to house some or all of

these mobility options — including from e-scooters to “flying cars”.

Below we illustrate four different customers and their likely mobility preference.

Customer #1 lives and works in the city and does not care for car ownership. The

customer prefers to rideshare around the city and its surroundings. However,

occasionally s/he wants to take a road trip or embark on a multiple-stop trip that

isn’t necessarily predictable (“hey, let’s stop there”). The customer is neutral

about driving — the option to drive would be desired under the right

circumstances but mostly the car would be used as a riding mechanism.

Market State Pre & Post Impact Market State Pre & Post Impact

Total U.S. Vehicle Installed-Base 233 Total U.S. Vehicle Installed-Base 126

U.S. AV Subs Installed Base 59 U.S. AV Subs Installed Base 126

Non-AV Subs Installed Base 174 Non-AV Subs Installed Base 0

U.S. Urban RoboTaxi Installed Base 6 U.S. Urban RoboTaxi Installed Base8

U.S. Light Vehicle Sales (SAAR) 14 What's Impacted? U.S. Light Vehicle Sales (SAAR) 0

AV Subs (SAAR) 11 AV Subs (SAAR) 8

U.S. Households 126 RoboTaxi TAM Expands (L5) U.S. Households 126

U.S. Drivers 223 Networks Integrate (RoboTaxi + AV Sub) U.S. Drivers 223

U.S. People Population 326 Vehicle density drops to 1/house U.S. People Population 326

U.S. Population Density 86 Non-urban consumers subscibe to Vehicles/Driver 0.6x

Vehicles/Driver 1.0x a single car and use sharing extra Vehicles/Household 1.0x

Vehicles/Household 1.9x needs. Shared vehicles sourced from

RoboTaxi fleets or AV Subs in what

becomes L5 Peer-to-Peer sharing

Addressable Market (Lifetime of Car) Addressable Market (Lifetime of Car)

1. Auto 1.0 TAM 1. Auto 1.0 TAM

Variable Profit @ Sale $8,500 Variable Profit @ Sale $8,500

Aftermarket (0-3yrs) $1,500 Aftermarket (0-3yrs) $1,500

Total Variable Profit $10,000 Total Variable Profit $10,000

Auto 1.0 TAM $1,744,400 Auto 1.0 TAM $0

2. Urban RoboTaxi AV TAM 2. Urban RoboTaxi AV TAM

RoboTaxi Lifetime Revenue $946,544 RoboTaxi Lifetime Revenue $1,306,052

RoboTaxi AV Profit TAM (18%) $170,378 RoboTaxi AV Profit TAM (18%)$235,089

3. AV Subs TAM 3. AV Subs TAM

AV Subs Lifetime Variable Profit $1,652,000 AV Subs Lifetime Variable Profit$3,528,000

Total: $3,566,778 Total: $3,763,089

Addressable Market (Annual) Addressable Market (Annual)

Auto 1.0 TAM $34,477 Auto 1.0 TAM $0

+ Urban RoboTaxi TAM $47,391 + Urban RoboTaxi TAM $65,390

+ AV Subs TAM $110,133 + AV Subs TAM $235,200

Total: $192,001 Total: $300,590

Post Stage 2 AV Subs

AV Subs Stage 2 (2030s+)

Post Stage 1 AV Subs


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Customer #2 lives in a rural area and commutes to work either in a city or locally.

This customer values the freedom of instant mobility and so rideshare isn’t a day-

to-day option. Rather rideshare is used for commuting and during city trips either

for work or pleasure. Perhaps the customer enjoys leasing vehicles and

occasionally does need a utility vehicle for a project or long trip with friends and

family. This customer, who might own two vehicles in the household, would

probably utilize a subscription model for one or both vehicles, and ridesharing on

occasion. The ability to integrate the subscription vehicle with the ridesharing

network would also be valued if it were easy.

Customer #3 lives in a rural area and works in both city and rural areas. S/he

utilizes pickup trucks for work either as a sole proprietor or small fleet. Instant

mobility freedom is very high priority and leasing isn’t often desired since the

vehicle undergoes significant wear and tear. Occasionally that customer does

find value in having access to a car temporarily. Here this customer might stick to

a traditional ownership model while subscribing to a subscription on demand for

occasions.

Customer #4 loves cars, particularly performance vehicles and those taking

advantage of new technology like heads-up displays (HUDs) and connected

infotainment. This customer would probably prefer to subscribe to a car and

enjoy a menu of offerings. Ridesharing would also come into play.

Figure 66. Illustrative Customer Profiles & Mobility Solutions


The key with this exercise is to show the widely varying mobility preferences that

will exist between regions — both urban/rural and good/bad weather — as well as

those customers who use a vehicle for utility versus those who use it to get from A

to B.


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Below we attempt to visualize the exercise by splitting the quadrants by segment

(Y-axis) and region/weather. For each colored section, we assign a mode of mobility

that will likely be accepted for that particular segment-regional mix. The color codes

signify the risk to auto sales — green being no/minimal risk, yellow signifying some

risk and red significant risk of lower vehicle sales.

Figure 67. Mobility Solutions and Impact on Auto Sales (Color-Coding) by Region, Weather &

Segment


How About the Suppliers?

For the suppliers, the content opportunity (ex. urban RoboTaxi, which is inherently

low volume by automotive standards) can be broken down into a number of

buckets:

1. The actual sensing suite itself (cameras/radars/LiDAR/sonar);

2. The compute/software stack including the chip hardware, electronics content,

and the associated software stack (perception algorithms, mapping, driver

policy, sensor fusion, cybersecurity) typically housed in a domain controller for

sophisticated systems;

3. A driver-monitoring system (DMS), which is increasingly becoming a must-have

solution for level-2+ and higher. For an AV Sub, this might actually expand to an

occupant-monitoring system;

4. Other vehicle-related content including signal/processing/functional safety

(electrical architecture, domain controllers), more advanced cockpit electronics

for improved human-machine interface (digital instrument clusters, heads-up

displays), redundant braking/steering, and data/connectivity/OTA/cybersecurity

content;

Figure 68. AV Building Blocks

Source: Aptiv

Cloud (OTA, telematics, data processing & analytics)

Application Layer (fusion, driving policy)

Middleware (systems integration, functional safety)

Operating System (systems integration, functional safety)

Hardware Abstraction (systems integration, functional safety)

Compute (domain controllers)

Data & Power Distribution (high speed/power)

Components (sensors, ECUs)


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5. Non-vehicle content such as mobility services platforms leveraging big data to

help networks optimize for key revenue and cost metrics. Examples of

companies involved in this field include Ridecell, rideOS, Coord and others.

6. The AVs themselves. Unlike the traditional supplier-automaker model, we

believe contract manufacturing could thrive in the era of AVs, for two reasons.

First, RoboTaxi AVs will likely be designed quite differently than traditional cars,

and even then not all RoboTaxis will be designed the same. For example, an

urban RoboTaxi might look very different than a RoboTaxi designed for a senior

living community or for providing lengthy trips between cities, including

overnight. Second, RoboTaxis are an inherently low-volume business, so an

automaker (or any player) looking to enter the market might prefer to share a

vehicle platform than produce themselves. To be sure, not everyone will adopt

this approach, particularly the early-movers who are trying to establish a

scaling advantage or to trying to design the vehicle around their sensors. But

we think outsourced manufacturing will make sense for large parts of the

industry. Magna’s role in this will be interesting to watch since the company

already has significant experience assembling vehicles for global automakers.

Of these four buckets, we view the compute/software stack as most important

because the software approaches inform the compute, which then informs the

choice of sensors.

Having expertise in the compute/software stack allows greater opportunity for cost

optimization, which is very important for a Tier-1 supplier’s competitive position.

How do you effectively update maps? How does your software approach influence

compute density and sensor selection, both of which impact costs?

For Tier-1 suppliers, the opportunity for AV Subs is to develop safe, reasonably

agile, scalable, accountable, and low-cost solutions for automakers. For the

automakers, the challenge is to build powerful network effects to leverage both the

increasing profit TAM that AVs promise and potential market share gains from

lagging automakers.

Figure 69 below shows our global ADAS/AV-related revenue estimates for Tier-1

suppliers. A couple of points about our assumptions: (1) The estimate spans

personal-vehicles only, not RoboTaxis, both to be a bit more conservative and to

reflect the uncertainty over how much of the RoboTaxi AV-related content will end

up with Tier-1s (we expect some of course, but perhaps less so than personal

vehicles given what key players are doing today); (2) We assume that ADAS

reaches “full” global penetration (~65% of light vehicle production, or LVP) by

2025E; (3) Our global LVP is assumed to decline from ~100 million units to ~87

million, in order to assume some impact from RoboTaxis. Frankly, this assumption

can be debated in either direction, particularly given our prior work showing that

frontier economies could enjoy significant gains (that offset declines in developed

economies) because AVs could significantly reduce the threshold required for

vehicle penetration (versus now). Our ~87 million assumes density declines in the

U.S. (consistent with our prior RoboTaxi county-level modeling), Canada, Europe,

and Japan, with no impact in other major regions but also no gains from

emerging/frontier economies either; (4) The simulation reflects our view that

automakers could trade up from basic-ADAS in two major waves. The first wave

(2020-2022) will be the move to level-2+ and some level-3, and the second wave to

level-4 AV Subscription models.


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Figure 69. Global ADAS – to Level 4 Penetration & Tier-1 Supplier Revenue TAM Forecast (LVP = Light Vehicle Production, Analysis for Personal

Retail Vehicles, Excludes Urban RoboTaxi TAM)


2019E 2020E 2021E 2022E 2023E 2024E 2025E 2026E 2027E 2028E 2029E 2030E 2031E 2032E

2019E 2020E 2021E 2022E 2023E 2024E 2025E 2026E 2027E 2028E 2029E 2030E 2031E 2032E

ADAS- AV Feature TAM

ADAS Penetration (%)

ADAS - Basic 34% 44% 41% 40% 27% 23% 21% 19% 17% 12% 12% 12% 12% 12%

ADAS + Level 2(+) 1% 1% 10% 12% 30% 35% 40% 40% 40% 40% 35% 30% 28% 28%

ADAS + Level 3+ (hwy L4) 0% 0% 1% 1% 2% 3% 3% 3% 3% 3% 3% 3% 3% 3%

L4 Features & AV Subs (Stage 1) 0% 0% 0% 1% 1% 1% 1% 3% 5% 10% 15% 20% 22% 22%

Total ADAS Penetration 35% 45% 52% 53% 60% 62% 65% 65% 65% 65% 65% 65% 65% 65%

No ADAS 65% 55% 48% 47% 40% 38% 35% 35% 35% 35% 35% 35% 35% 35%

L3-L4 Premium Penetration 1% 2% 16% 11% 29% 40% 42% 61% 79% - - - - -

ADAS Penetration (units)

Global LVP 100 100 100 100 100 100 98 96 94 92 90 89 87 87

No ADAS 65 55 48 47 40 38 34 34 33 32 32 31 30 30

Global ADAS Penetration 35 45 52 53 60 62 64 62 61 60 59 58 56 56

YoY 29% 16% 2% 13% 3% 3% -2% -2% -2% -2% -2% -2% 0%

ADAS - Basic 34 44 41 40 27 23 21 18 16 11 11 11 10 10

ADAS + Level 2(+) 1 1 10 12 30 35 39 38 38 37 32 27 24 24

ADAS + Level 3+ (hwy L4) 0 0 1 1 2 3 3 3 3 3 3 3 3 3

L4 Features or AV Subs (Stage 1) 0 0 0 1 1 1 1 3 5 9 14 18 19 19

Global LVP - Premium Segments 9 9 9 9 9 9 9 9 10 10 10 10 10 10

ADAS Tier-1 CPV

ADAS - Basic $150 $150 $125 $125 $100 $100 $100 $100 $100 $98 $96 $94 $92 $90

ADAS + Level 2(+) $800 $800 $800 $775 $750 $740 $725 $710 $695 $681 $667 $654 $641 $628

ADAS + Level 3+ (hwy L4) $2,000 $2,000 $2,000 $1,750 $1,600 $1,550 $1,550 $1,550 $1,500 $1,470 $1,441 $1,412 $1,384 $1,356

L4 Features & AV Subs (Stage 1) $6,000 $6,000 $6,000 $6,000 $5,800 $5,700 $5,600 $5,500 $5,300 $5,200 $5,125 $5,000 $4,900 $4,802

ADAS Tier-1 Revenue TAM

ADAS - Basic $5,085 $6,570 $5,075 $5,000 $2,730 $2,330 $2,058 $1,825 $1,600 $1,085 $1,042 $1,000 $961 $942

ADAS + Level 2(+) $800 $800 $8,000 $9,300 $22,500 $25,900 $28,420 $27,275 $26,165 $25,129 $21,117 $17,384 $15,582 $15,271

ADAS + Level 3 $200 $400 $2,000 $875 $3,200 $4,650 $4,557 $4,466 $4,235 $4,068 $3,907 $3,752 $3,603 $3,531

L4 Features & AV Subs (Stage 1) $0 $0 $2,400 $3,000 $4,060 $3,990 $5,488 $15,847 $24,942 $47,963 $69,489 $88,584 $93,584 $91,712

Total TAM $6,085 $7,770 $17,475 $18,175 $32,490 $36,870 $40,523 $49,413 $56,942 $78,244 $95,554 $110,720 $113,730 $111,456

YoY 28% 125% 4% 79% 13% 10% 22% 15% 37% 22% 16% 3% -2%

2019E 2020E 2021E 2022E 2023E 2024E 2025E 2026E 2027E 2028E 2029E 2030E 2031E 2032E

ADAS Basic Content

Camera $45 $45 $44 $43 $42 $41 $40 $39 $39 $38 $37 $36 $36 $35

Radar $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0


Other $60 $61 $37 $39 $15 $16 $17 $19 $20 $19 $18 $17 $17 $16

Total: $150 $150 $125 $125 $100 $100 $100 $100 $100 $98 $96 $94 $92 $90

ADAS + Level 2(+)

Cameras (2-3x) $135 $134 $131 $128 $126 $123 $121 $118 $116 $114 $111 $109 $107 $105

Radar (3x) $200 $198 $196 $194 $188 $184 $181 $177 $174 $170 $167 $163 $160 $157


DMS $150 $149 $147 $146 $141 $138 $136 $133 $130 $128 $125 $123 $120 $118

Other $40 $48 $56 $40 $36 $40 $39 $38 $36 $36 $35 $34 $34 $33

Total: $800 $800 $800 $775 $750 $740 $725 $710 $695 $681 $667 $654 $641 $628

ADAS + Level 3+ (highway L4)

Cameras (1-5x) $180 $178 $175 $171 $168 $164 $161 $158 $155 $152 $149 $146 $143 $140

Radar (5x) $300 $297 $294 $291 $282 $277 $271 $266 $260 $255 $250 $245 $240 $235

LiDAR (0-1x) $350 $350 $350 $200 $196 $192 $188 $184 $181 $177 $174 $170 $167 $163


DMS $150 $149 $147 $146 $141 $138 $136 $133 $130 $128 $125 $123 $120 $118

Other $370 $383 $397 $312 $201 $179 $206 $233 $210 $205 $201 $197 $193 $189

Total: $2,000 $2,000 $2,000 $1,750 $1,600 $1,550 $1,550 $1,550 $1,500 $1,470 $1,441 $1,412 $1,384 $1,356

AV Subs

Cameras (12x) $513 $503 $493 $483 $474 $464 $455 $446 $437 $428 $420

Radar (8x) $550 $534 $523 $512 $502 $492 $482 $473 $463 $454 $445

LiDAR (3-4x) $1,050 $1,050 $1,050 $998 $948 $900 $855 $812 $772 $733 $697

Compute/Software $2,500 $2,425 $2,377 $2,329 $2,282 $2,237 $2,192 $2,148 $2,105 $2,063 $2,022

DMS $146 $141 $138 $136 $133 $130 $128 $125 $123 $120 $118

Other $1,241 $1,147 $1,119 $1,142 $1,161 $1,077 $1,088 $1,121 $1,100 $1,102 $1,102

Total: $6,000 $5,800 $5,700 $5,600 $5,500 $5,300 $5,200 $5,125 $5,000 $4,900 $4,802

Total

Cameras (12x) $1,679 $2,121 $3,257 $3,594 $5,606 $6,110 $6,512 $7,088 $7,608 $9,229 $10,375 $11,415 $11,520 $11,290

Radar (8x) $230 $257 $2,254 $2,749 $6,585 $7,653 $8,386 $9,019 $9,588 $11,432 $12,362 $13,200 $13,187 $12,923

LiDAR (3x) $35 $70 $350 $625 $1,127 $1,311 $1,531 $3,262 $4,747 $8,379 $11,487 $14,127 $14,439 $13,730

Compute $1,866 $2,352 $5,123 $6,514 $11,866 $13,337 $14,626 $18,359 $21,771 $30,835 $38,291 $45,109 $46,526 $45,600

DMS $165 $178 $1,617 $1,892 $4,617 $5,354 $5,847 $5,870 $5,883 $6,238 $5,991 $5,754 $5,526 $5,416

Other $2,111 $2,792 $2,474 $2,801 $2,689 $3,104 $3,622 $5,816 $7,345 $12,131 $17,048 $21,116 $22,532 $22,497

Total: $6,085 $7,770 $15,075 $18,175 $32,490 $36,870 $40,523 $49,413 $56,942 $78,244 $95,554 $110,720 $113,730 $111,456

Total Sensors $1,944 $2,448 $5,861 $6,968 $13,318 $15,074 $16,429 $19,368 $21,942 $29,040 $34,223 $38,741 $39,146 $37,943


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AV Technology—Building an AV Autonomous vehicles are often classified based on the levels of capable autonomy,

with level-5 representing the highest possible autonomy and level 0/1 the lowest. In

reality, the other important classification to consider is the domain in which the AV is

designed to operate. For example, designing an AV to operate in a major city

(“RoboTaxi”) presents a very different set of challenges than that of a highway pilot

feature. Cities are generally considered more difficult but each domain has its own

set of challenges. As a result, each domain requires a somewhat different

optimization for sensors, computing needs, testing/validation, and costs.

The Basic Components of Autonomous Driving

In the simplest form, achieving automated driving can be thought of as a (really

complicated) two-step process:

1. Sensing, which includes mapping/localization; and

2. Driving Policy which includes path planning, reasoning/prediction, and vehicle

controls.

There’s both a hardware component — physical sensors, compute, electrical

architecture, redundant systems — and a software component to AV.

The ultimate goal is to optimize first and foremost for safety (an above-human

safety level as an initial minimum requirement), agility, accountability, and costs. In

some AV models like urban RoboTaxi, cost optimization is less crucial at this stage.

Sensing/Perception

Sensing is all about forming an accurate and detailed environmental model of what

is around you at ideally above-human level capabilities. At the hardware level, this is

mostly accomplished through three sensing modalities—cameras, radars, and

LiDAR. The choice between the three sensors often boils down to the required

feature application (from ADAS-to-AV), the targeted vehicle domain, and the

associated computing needs and systems costs. For any sensor, key metrics to

consider include a sensor’s resolution, range, field-of-view, reliability, and costs.

Basic ADAS & Level-2 Systems

A basic ADAS system (think automatic emergency braking plus lane keep assist)

can often be accomplished with a single sensor, most often a camera. Cameras

enjoy a number of exclusive sensing advantages including all-important lane and

free space monitoring. Cameras, particular monocular, also enjoy relatively lower

costs that make them a popular choice for automakers looking to meet ADAS

regulations.

At the onset, ADAS actually began as a radar-only feature because of the initial

need to detect moving metal objects (cars), something radar does very well through

all weather and lighting conditions. Even today you can still find some radar-only

adaptive-cruise-control systems out there. As mono cameras began encroaching on

radar capabilities by beginning to accurately detect cars too (around 2012), we

began seeing more automakers using them in lieu of radars. A key advantage

cameras have over radars is the ability to detect lanes, so cameras became a sort

of one-stop-shop for automakers needing to meet ADAS regulations. Indeed, some

notable automakers have deployed camera-only ADAS and even level-2 systems —

GM, Subaru, Nissan (level-2 ProPilot), and Audi.


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The key to a successful camera-only ADAS system is the software itself—superior

detection capability equals greater comfort in relying on a single sensor. This has

been achieved through classical computer vision techniques (annotating images)

and more recently, through deep learning, which has been particular useful for pixel-

level labeling techniques to aide in free space detection.

Still, the majority of automakers are still opting for a camera/radar fusion for basic

ADAS/level-2 features, for two reasons: (1) fusion compensates for areas where

vision is vulnerable, such as low light or poor weather conditions; and (2) fusion

affords automakers greater comfort in offering level-2 features such as adaptive-

cruise-control with steering assist, which are becoming more popular. Technically,

the sensor fusion aspect is fairly straightforward since the mission is well-defined

and the sensors’ strengths/weaknesses are well-known. The key is to avoid false

positives and false negatives — when the ADAS systems either initiates a braking

action when it shouldn’t, or fails to detect an obstacle that requires braking. For

example radar-only systems are known to sometimes falsely detect a road barrier

(metallic object) or overhead bridge as obstacles because today’s radars cannot

classify and distinguish objects the way cameras can. Today’s radars also cannot

see lanes and can struggle with static metal objects such as a car stopped in front

of you at a red light. A camera-radar fusion system helps solve for these issues

since, in the case of the road barrier, both sensors wouldn’t agree that an AEB

event should occur. At the same time, in low-light or poor weather conditions, the

radar can cross-check the camera’s detection of obstacles ahead. It is notable that

some automakers offer both a camera-only and fusion solution depending on the

vehicle. GM, for example, offers a camera-only adaptive-cruise-control feature (we

believe utilizing a Mobileye EyeQ3 chip) as well as a more advanced version that

feature that leverages fusion.

Level-2+ Systems

A level-2+ feature — where a driver can take both their feet and hands out of the

driving equation in certain domains (with a driver-monitoring system ensuring eyes

are engaged) — requires superior range and field of view from a sensing

perspective, as well as software that’s capable of some prediction and path

planning. This, we believe, necessitates multi-focal cameras (2-to-3), a few radars

(1-to-3 in the front and front-sides), and mapping technologies to augment onboard

sensors both in scene perception and interpretation (how many lanes, where does

the road split, tracking human drivers’ prior paths). Level-2+ expands the sensing

challenge to areas like complex free space detection for small objects (Can I drive

over that? Do I need to avoid it?), path delimiters, traffic lights, and general

obstacles such as construction zones. It also must anticipate vehicles that might

cut-in (so detecting vehicle intent or turn signals).

Although level-2+ is technically not autonomous driving (the driver is expected to be

in the loop at all times), the expanded list of sensing challenges stems from the

following dilemma that we believe has become more apparent over the past year.

Even though drivers are expected to remain attentive in level-2+, and even if the

DMS system confirms they are, they might still not know when they actually need to

take over. Many of today’s systems are highly capable in detecting certain objects

(lanes, cars, even people) but far less in detecting others (animals, a block of ice on

the road). So a perfectly attentive driver might not realize that the block of ice on

the road isn’t being detected, until it’s too late. This is an issue in some of today’s

level-2+ features on the road. In other words, not all handover events are

straightforward or solvable with a robust human-machine-interface or DMS, unless

that driver is fully knowledgeable about system limitations.


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For this reason, we have seen some level-2+ systems that restrict speed (traffic-

jam-assistant), restrict domain (divided highway, no lane change) and add sensors

and mapping to improve performance. Level-2+, in our view, is where high-definition

(HD) maps and crowdsourced maps start to become must-have content, and where

the sensing capability must improve to compensate not only for the risk of driver

inattentiveness, but for a lack of familiarity with system limitations. To be sure,

improvements in sensing and particularly mapping are expected to address these

challenges.

Sensing for AVs

Full AVs (level-4) are expected to require all three sensing modalities for added

redundancy and robustness. But even here, the sensing suite on an urban

RoboTaxi is likely to differ materially from that of an AV Feature (highway piloting) or

a future AV Subscription vehicle. RoboTaxis are expected to be the most sensor rich

due to their more complex operating domains, an earlier expected deployment and

a lesser focus by industry players on cost optimization at the onset. Most RoboTaxis

we have examined are fitted with multiple LiDARs (2-5x) cameras (9-14x), and

radars (6-24x). An AV Feature vehicle (highway autopilot) would most likely be

equipped with 3-8 cameras, 5-6 radars, and at least 1 LiDAR. An AV Subscription

vehicle would likely step up to 8-12 cameras, 6-8 radars, and 3-4 LiDAR sensors

though each sensor could vary depending on range/resolution/cost requirements.

Some of the sensing challenges for full AVs include:

– Distinguishing whether a person next to a bike is walking the bike or riding it;

– Clustering, or accurately detecting two people standing next to each other (or

a person standing right next to a car) as two separate vulnerable road users

(VRUs);

– Interpreting scene context, such as hand signals from a traffic officer or

another driver signaling with you in a 4-way stop intersection. Another example

is an emergency vehicle or a swiftly erected construction zones;

– Very poor weather including fog and heavy snow, or unusual (and sometimes

regional) edge cases like love bug season (the insects tend to drift into

oncoming traffic) in Florida;

– Oncoming vehicles particularly during unprotected left turns with pedestrians

crossing in a crowded setting;

– Cargo falling from a truck or a sudden appearance of other road debris;

– Finding an exact pickup point for a rider in the middle of the street or at a

house, or navigating a parking lot to find somebody;

– Complex or unusual free space detections at a far distance.

None of these challenges, perhaps with the exception of fog, are thought to be

beyond solvable. But there are different approaches with regards to software

development, fusion, compute management, AI techniques, and of course the

selection of the domain itself (i.e. simply avoiding environments where these

complexities are common).


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There are no easy shortcuts to sensing challenges. One school of thought (that has

more recently faded to some degree) argues a single neural-network could handle

the sensing challenge as opposed to annotating individual objects. Given the need

for effectively 100% detection accuracy as well as the need to dissect sensing

errors (a more difficult task with a single neural-net), we have seen companies

migrate away from conquer-all solutions towards those that combine classical

approaches, newer detection methods where they make sense (like neural nets),

and sensor fusion. With some background of sensing/perception and how it applies

to various ADAS-AV applications, we’ll now review the sensors themselves.

Radar (RAdio Detection And Ranging)

Radar uses emitted microwaves and reflected signals to detect objects and

measure their angle/position, range distance, and speed using the Doppler effect.

Automotive radars typically consist of a transmitter that generates a radio-

frequency, a receiver, associated antennas, and signal processing. Automotive

radar is commonly classified by its frequency and range/resolution capability—long-

range front-facing (77GHz), short-range corner (24GHz), and an emerging 79GHz

frequency (short-range/corner with high resolution). The 79GHz band (in a 77-81

GHz range) is expected to replace 24GHz ultra-wide-band. Automotive radar also

tends to operate under a frequency modulated continuous wave (FMCW) because

of superior range resolution and power requirements. The optimization challenge for

radars is to maximize resolution and range, while minimizing the noise-to-signal

ratio. Newer approaches to beam forming are attempting to solve for these

tradeoffs.

The unquestionable benefit of radar is its ability to operate in adverse weather

conditions, operate at night, accurately detect distance, accurately detect relative

velocity of an object, and even detect objects in front of other objects, which is very

handy in corner situations. This unique position earns radar a must-have position for

most (if not all) high-functioning semi-autonomous systems (level-2+) and full AVs.

During the initial onset of ADAS in the early-2000s, radar was a natural first choice

sensor because of its ability to detect metal objects in a manner that’s unaffected by

weather or lighting conditions. As a result, radar has and is still used extensively in

side-facing applications like blind spot warnings where detection of metal objects in

varying weather conditions is critical. Over the years, the industry also began using

radar for forward-facing applications including forward-collision warning and

adaptive cruise control. But forward-facing applications are where radar technology

began to show its weaknesses. First, traditional automotive radar has been

inherently less sensitive to non-metal (i.e. pedestrians, objects) and stationary

objects — both critical in forward facing applications. Because radar cannot actually

“see”, it cannot perform core forward-facing tasks like lane-departure warning, path

planning, and traffic sign/light recognition. Lastly, classic automotive radar isn’t

actually able to classify objects (i.e. this is a vehicle, this is a bridge), hence radar

has been prone to false positives resulting from a high noise-to-signal ratio. We

have even seen recalls related to this in the past.

Automakers have compensated for these shortfalls with cameras, but the radar

industry is rapidly moving to improve resolution/interference — particularly for AVs

where the role of radar becomes even more important. The higher resolution is

necessary to classify objects including pedestrians, cars, trucks, and cyclists,

analyze free space, and achieve higher angular resolution (distinguish two similar

sized objects near each other at equal distance), all at an adequate range and an

affordable cost and power consumption.

Figure 70. Forward Looking Radar

Source: Aptiv


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We have seen a number of approaches to achieving this, from multiple

input/multiple output (MIMO) technologies (Magna ICON) to new materials and

beam approaches (Metawave). The so-called imaging radars are expected to enter

production in the 2020-2022 timeframe.

We expect next-generation radars to play an increasingly important role in AVs

given what appears to be step-function improvements slated to enter production for

the next few years. Imaging radar fused with robust mapping might give automakers

some peace of mind with respect to robust system redundancy. This is particularly

important for AV Subs in a night time setting. Still, radars are expected to have

lingering resolution limitations relative to other sensors, such as determining what

type of car is being detected or whether a driver in the car is waiving your car

through a 4-way stop sign. Over the longer-term, it’s possible that imaging radars

might compete with LiDARs once automakers start focusing more intensely on

reducing AV systems cost, since radar’s weather performance will always give it

some advantage.

Figure 71. Profile of Selected Automotive Radar Companies

LR HR Radar Company

ADAS Radar Specs ADAS Radar Features Technology Time to Market

Arbe Robotics - >300m range

- 100° aperture (field of view)

- 1° angular resolution

- 1.25° azimuth

- Doppler resolution: 0.1 m/s

- Low cost, power, weight and small size sensors

- Can detect on -coming vehicles up to 120km/h

- Object identification/classification

- Detects, long-, mid-, and short-range objects

- FMCW mm wave radar

- 'Smart Sensor Fusion' - points other sensors to specific objects (reduces power consumption)

- Expected launch: 2019

- 7 customers are currently testing prototype

ARTsys360 - 150m range

- 360° view

- 30° elevation; -15° declination

- Small size: ~57mm x 50mm

- Mounted on top of vehicle

- 77 GHz micro radar

Echodyne - High-performance phased-array radar

- Beam-steering

- "MESA": metamaterial electronically scanning array

- 24 GHz

Prototype used on drone testing

Ghostwave - Radar system that is less susceptible to interference from other radars on the same frequency

- Uses a pseudo-random radio frequency generator

- 24GHz

Imec - Range: 30m

- 120° viewing

- Angular Resolution: 7.5cm

- Max speed; 50km/h (30mph)

- Robust radar; works in various adverse weather conditions

- Can be mounted invisibly (aesthetics, privacy)

- Very small form factor

- 79 GHz

- 28nm CMOS mm-wave chips

Lunewave - 300m

- 360° viewing

- Interference avoidance algorithm

- "best in class" resolution

- Cheaper and easy to produce (3D printed)

- 76-81 GHz mm wave and mirowave systems

- uses 3D printing to create new antenna architecture enabling more power

Prototype phase

Metawave

(Hyundai Investing)

- Detect autos at ~300m

- Detects pedestrians/cyclists at ~180m

- Beam-steering

- Core AI engine can discriminate objects

- Non line-of-sight "seeing"

- Analogue radar

- Metamaterials

-77 GHz – uses Infineon chipset

- NVIDIA AI processing engine

Oculii - 200m - Tracks 200 targets simultaneously

- Radar combines info w/ cameras around vehicle

- 77 GHz silicon SoCs

Steradian Semiconductor

- All-weather 4D mapping device

- Increase RF output power along w/ reducing noise figure

-Size: 28nm mm wave imaging radar - 79 GHz

Uhnder

(partnered with Magna)

- >300m range - Can track ~100x more objects than competitive systems

- Able to identify both static and dynamic objects

Has prototype product

Vayyar Imaging - "Adaptive Collision Avoidance": object detection/classification, trajectory mapping; monitors surroundings for static and dynamic objects; identifies and avoids elevated obstacles

- mm wave radar

- 78-81 GHz

Partnered with Faurecia and Valeo



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Figure 72. Auto Radar Sensor Summary


Cameras

Cameras, both mono/stereo, have the inherent advantage of processing extremely

rich and dense amounts of data in a similar way the human eye can, though the

human eye can be thought of as a sort of supreme “camera” when it comes to

resolution and range rate. Cameras also always had the inherent advantage of

being low cost. Of course, the challenge they historically faced was that “seeing”

required significant software ingenuity (machine vision, deep learning) and powerful

yet efficient computing. Another challenge was to do this all on lower-cost

monocular cameras as opposed to stereo, the challenge being in mono’s inability to

detect in 3D and achieve radar-like distant measurement. Even before the

advances that occurred in vision software/computing power, cameras enjoyed an

advantage of sole detection capabilities (vs. radar) in important areas like lane-

departure warning (LDW), traffic sign/light recognition (TSR/TLR), and object

classification. So if an automaker wanted these features, it meant that a camera

was a “must-have”, in addition to radar. Of course as ADAS regulations began to

take shape, the industry challenged itself to reduce systems cost, and the natural

path was to attempt to migrate to camera-only solutions.

Within cameras there was the option of directing resources to either stereo or

monocular (mono) vision — two very distinct approaches. Initially, there was a

thought that stereo — which uses two cameras to triangulate a good short-range 3D

image — would provide better detection worthy of the added weight and cost. For

an industry racing to gain an early mover ADAS advantage (mainly in luxury

vehicles), stereo was an easier choice early on. Monocular, or a single camera, was

initially seen as relevant for lane detection but less so for forward object detection,

mainly because of the inability to measure distance the way radars and stereo

cameras could. Thanks to advancements in computer vision and deep learning,

around 2012 the monocular camera achieved production-worthy forward-collision

detection capabilities with adequate distance measurement — after all, humans

don’t measure distance when we drive but rather infer from the size/position of an

object in front of us. This leap allowed mono to emerge as the sensor of choice for

ADAS systems. As mentioned earlier, today some automakers utilize camera-only

solutions for ADAS and even level-2+, though the majority of automakers still opt for

camera-radar fusion.

Radars

Strengths Weaknesses Key Players What We’re Watching

All Weather

Operation

Object

Classification

Tier-1’s - Imaging radars in

development

Distance

Accuracy

Static Objects

(noise/signal)

Infineon/NXP/TI - Cost of next-gen radars

All Lighting

Conditions

Relative

Resolution

Metawave, Arbe,

other startups

Costs Free Space

Detection/Lanes


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We expect the monocular camera to remain the dominant sensor for basic ADAS

applications. The next generation of mono cameras will continue to improve upon

their resolution (pixel-per-degree) and field-of-view. Level-2+ and higher

applications will require multi-focal cameras around the car, with at least 2-3 in the

front of the car. Where two monocular cameras overlap, distance measurements

can also improve with stereo-vision methods applied to the overlapping images. Of

course, stereo can do this too and a number of Tier-1 suppliers are still developing

stereo systems (i.e., Veoneer), but the pushback is that if you are going to add a

camera you might as well gain range and/or field-of-view, which is better achievable

with multiple mono cameras. Level-4 AVs are also expected to have 8-12 cameras

around the vehicle.

Owing to its inherently high resolution, ability to ascertain context and ability to read

traffic signs/lights, cameras are expected to migrate their dominant role in ADAS

towards a similarly important role in AVs.

Figure 73. Auto Vision Sensor Summary


Spotlight on CMOS Sensors for ADAS

In autonomous driving and ADAS a variety of sensors are required for the

surrounding conditions. High-end complementary metal oxide semiconductor

(CMOS) image sensors and high-precision image recognition protocol are needed

for automatic braking, automated lane keeping, recognition of information such as

traffic signals, automation of driving actions such as turning and stopping, and for

high-speed driving and responding to changes in conditions caused by weather or

tunnels, for example.

In 2017 the automotive CMOS image sensor leaders were ON Semiconductor, with

a 65% share of the market and Sony with around 15%. Sony’s presence was close

to zero until around 2016 when the company ramped up adoption and rapidly

increased its share. One notable change in the market has been Toyota’s switch

from ON Semiconductor to Sony as main image sensor supplier.

Vision


Highest

Resolution

Poor Weather

Conditions

Tier-1’s - Entry of higher resolution cameras

Drivable Path

Classification

Precise Distance

Measurement

Mobileye (Intel),

ST Micro, NVIDIA

- Neutral net advancements

Scene Context,

Traffic

Lights/Signs

Poor Lighting

Conditions

Sunny Optical,

ON, Sony,

OmniVision,

Costs


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Automotive CMOS image sensors are only around 10% of the total CMOS image

sensor market by volume and their sensing performance and quality differs from

that of the smartphone CMOS image sensors that make up the lion’s share of the

market. At first, low-pixel (~1 megapixel) products with low dynamic range and color

reproducibility (color quality) were the main products and they were used for limited

applications such as rear safety checks.

Medium-pixel (2-4 megapixel) products with some degree of dynamic range

debuted for ADAS cameras around 2017. The higher pixel density enabled them to

detect more distant objects and information while the improved dynamic range

increased the precision of the response to light/dark changes. Improvements in

software were a boost for image analysis, quality of judgement, and speed.

As a result of these improvements in CMOS image sensor quality, ADAS functions

have been extended from automated braking at low speeds to lane keeping and

braking at medium speeds. However, performance still needs to be upgraded and to

this end new sensors are being adopted.

Figure 74. ADAS Camera Unit (Toyota Prius Safety Sense P-front Camera)

Source: Fomalhaut Techno Solutions, Citi Research

The latest automotive CMOS image sensors to reach the adoption stage and mass-

production schedules are 5Mpxl-10Mpxl and they have a dynamic range of 100 to

140 decibels. These next-generation products are intended to enable image

recognition when light conditions change suddenly — when a vehicle emerges from

a tunnel, for example — detection of obstacles and people ahead at night, and

detection of smaller objects at greater distances.


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LiDAR seems to be the next candidate for installation in mass-produced vehicles.

LiDAR projects light from a device and analyzes the light that is reflected back to

the device. Its spatial grasp is better as it can gauge the distance to an object more

accurately than image sensors and some products achieve 3D by projecting light on

a broader axis. Extension of distance is a focus in current technological

development. Wavelength is being switched to ultra-long measurement and new

photodiodes (avalanche and silicon) are being used to increase the light-receiving

element’s precision.

LiDAR

LiDAR stands for Laser Imaging Detection Ranging. As its name suggests, LiDAR

emits laser light and analyzes the reflection in similar time-of-flight (ToF) concept as

radar (through an emitter, receiver and signal processing). In the past relatively

simple short range 3-beam LiDAR sensor were used for autonomous braking at

low-speeds, mainly in Europe.

LiDAR drew significant interest for having its own set of advantages vs. cameras

(night detection) and radar (higher resolution, 3D depth-sensing) — all with fairly

good range. As a result, we have seen some industry AV software startups utilize

LiDAR very prominently, including at times as a primary sensor. The interest in

LiDAR naturally created dozens of LiDAR companies each utilizing a somewhat

different approach, or attacking a different set of challenges.

A LiDAR sensor consists of an emitter, detector, and processing/interpretation. With

each there are a number of approaches and industry players. For example, emitter

solutions include vertical cavity surface-emitting lasers (VCSEL) and edge emitting

lasers (EEL). Receivers or photo-detector methods include avalanche photodiodes

(APD) or single-photon avalanche diode (SPAD), depending on the required

optimization of the sensor.

Like all sensors, the key measurements of performance include resolution (in

LiDAR’s case, a 3D point cloud), range and range measurements/second, cost, and

power consumption. Not all performance requirements are created equal — for

example a RoboTaxi might favor high resolution over range whereas a level-4

highway system might lean more on range.

For an AV Sub, LiDAR would be a critical sensor to ensuring safe night time

performance and detection at long-range. Given the varying sensing requirements

and the fact that, unlike radars/cameras, LiDAR penetration is very low and costs

are very high, a number of LiDAR sensor approaches have emerged.

The first is the mechanically moving mirror LiDAR that’s best known as Velodyne’s

product first featured on the Google Car (the spinning 64 beam laser on top of the

car) and can still be found on GM-Cruise’s test fleet (five 32-beam LiDARs on top of

the car), Ford’s AV fleet (2 on top of the car) and other industry players including

Uber and Voyage (which recently migrated to Velodyne’s new 128-beam sensor).

The sensor covers 360 degrees around the car at high resolution. The biggest

drawback is cost and to some extent reliability/industrialization, though this is an

area Tier-1 suppliers and even automakers are helping to solve (Ford is an investor

in Velodyne). The other mechanic LiDAR currently in the market is the Valeo/ibeo

Scala which is featured on the Audi A8 traffic jam assist function, and operates at

level-3. That system also leverages vision and radar that’s fused in a multi-domain

controller.

Figure 75. Luminar LiDAR Output

Source: Volvo Cars Site


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The other emerging mechanical LiDAR comes from Luminar, which uses a more

powerful 1550nm approach leveraging indium gallium arsenide (InGaAs) material in

the receiver as opposed to silicon. The result is a far more powerful LiDAR sensor

with long-range capability, something that’s critical for AV Subs but also urban

RoboTaxis. A few years ago the pushback to this approach was that it would prove

too costly, but Luminar also invested in production capabilities in Florida, and our

conversations with the company suggest its costs (relative to other LiDAR

approaches) will not be an issue for volume commercialization. This has been

supported by partnerships with Toyota and Volvo. In November 2018, Volvo and

Luminar showed impressive long-range detection (250 meter) of a pedestrian’s

arms and legs, which is the level of resolution required to understand context. In

December 2018 Luminar announced a collaboration with Audi’s Autonomous

Intelligent Driving (AID) division to deploy long-range LiDAR as part of AID’s urban

AV development with target deployment in 2021. AID’s test vehicles are equipped

with two Luminar LiDARs (each with a 120-degree field-of-view)

The other class of LiDAR sensors is solid-state, which aim to reduce costs and

improve system reliability. Within solid-state there are a number of approaches

mainly around how the laser beam is distributed and controlled during illumination.

One approach that has gained momentum is the MEMS-based scanning mirror, an

approach used by Innoviz, which in April 2019 was selected by BMW for AV

production in 2021 (Magna Tier-1). The Innoviz LiDAR is based on 905nm laser

light with a 250 meter detection range.

Another solid-state approach is the optical phased array (OPA), pioneered by

Quanergy. The inherent advantage here is that there are truly no moving parts

thereby making a stronger case for durability. Still, Quanergy has yet to

commercialize this in the automotive market though it appears to be making

progress in non-automotive verticals where LiDAR is also used. Other companies

pursuing a MEMS approach include LeddarTech and Aeye.

Flash LiDAR is another approach that doesn’t scan a laser beam but rather

illuminates an entire scene at once. This too is a solid-state solution. Flash LiDAR

outputs an impressive camera-like image but is limited to fairly short-ranges, making

it perhaps a suitable sensor for the side of the vehicle as opposed to the front (at

high speed).

Figure 76. Select Automaker/Tier 1 Supplier LiDAR Relationships

Select Automaker/Tier-1 Supplier Select LiDAR Relationships

GM-Cruise Velodyne, Strobe (acquired)

Ford Velodyne, Princeton Lightwave

Aptiv Innoviz, LeddarTech, Quanergy

Volvo Luminar

Toyota Luminar

Uber Velodyne

BMW (Magna Tier-1) Innoviz

Audi AID Luminar

Veoneer Velodyne



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The LiDAR challenge is not only about desired performance, but also about costs

and reliability. What’s been interesting is that despite the dozens of LiDAR

companies that exist, we have seen a few automakers pursue M&A to bring LiDAR

in-house—such as Ford/Princeton Lightwave and GM/Strobe. We think this type of

M&A likely reflects a combination of a technology call with prospects for faster

industrialization into a complete system, and therefore lower cost and higher

reliability.

Figure 77. Auto LiDAR Sensor Summary


Sensor Fusion

With the exception of basic-ADAS and some level-2 systems, we expect all level-2+

or higher features to incorporate a fair amount of sensor fusion. From ADAS to most

level-2+ systems, that fusion will most likely involve cameras and radar. When

moving towards level-3 and full AVs, fusion will most likely include all three sensing

modalities. Within sensor fusion, there are a number of approaches each attempting

to optimize for superior detection capability at the lowest sensor/compute cost and

power consumption. There are a number of schools of thought around sensor fusion

for AVs.

One approach is to extract all of the raw data from the individual sensors (cameras,

LiDARs, radars) and leverage AI/machine learning to construct a detailed

environmental model from that raw data. The thought with this approach is that you

can train a super-human perception system that uses AI to ultimately extract the

absolute best from all of the sensors, after which the resulting environmental model

is localized with an HD map. There are two pushbacks to this approach. The first is

that it’s very computationally intensive and ultimately more expensive. The other is

that it’s more difficult to ascertain where a failure might have occurred. Still, several

players are pursuing this approach particularly within the RoboTaxi AV domain.

The other school of thought within sensor fusion argues that a single sensor should

be heavily trained to take on a primary role in detection/prediction/localization—

most often surround cameras or LiDAR. The non-primary sensors would then

mainly serve the role of detection redundancy, particularly in areas where the

primary sensors are less capable. The approach of Tesla and Mobileye, for

example, appear more in-line with this school of thought.

LiDAR


Higher

Resolution vs.

Radar

Costs Tier-1’s - Cost vs. Resolution

3D Depth

Sensing

Weather

Vulnerability

Velodyne, Luminar,

Innoviz, Waymo,

LeddarTech, Oryx

- Internal OEM

Developments

(i.e.. GM Strobe)

All Lighting

Conditions

Durability/

Packaging

Osram, Hamamatsu - Future OEM Awards


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Pronto.ai, Anthony Levandowski’s new startup, also appears to hold the view that

the solution isn’t more sensors (beyond cameras, 6 in the case of the company’s

current testing) but rather “much better software”. The upside here would come from

a more scalable, cost effective solution that is also arguably more transparent.

Indeed, while there are a number of shortfalls in Tesla’s sensing suite (in our view),

cost is not one of them. So if the software/fusion proves sufficient, the resulting

systems cost advantage could yield a meaningful competitive advantage.

Ultimately, the ability to lower costs and power consumption will become key

competitive considerations in the AV field, particularly for those looking to sell AV

Subs where cost optimization will be vital to enter the market (unlike RoboTaxis

where you can initially scale a less cost-optimized system in order to build that all-

important network effect).

The mapping aspect of sensing has also gained importance in recent years.

Although human drivers don’t need maps to drive, we tend to be more comfortable

driving on roads we already know. For automated driving (level-2 through AVs),

mapping can be thought of as another redundancy layer for precise localization

(Where am I?), path delimiters (What’s around me? What’s coming next?), and

drivable paths (where can I go? what are my options?). Traditional navigation (GPS)

maps can localize a vehicle to ~10m range, which isn’t accurate enough for

autonomous vehicles. Detailed HD-Maps and 3D maps are able to map at a high

detail with centimeter scale. Today, the issue with HD Maps isn’t so much about

creating them but rather updating them. There are a number of approaches to this

including crowdsource mapping that leverages existing onboard sensors (either

RoboTaxis themselves, or ADAS cameras on retail vehicles). The set of collected

data would sit on top a typical navigation map (or HD map) to create an effective

high-resolution map that can help with road hazards, traffic flow, predictive routes,

environmental information, and many other features. We believe such

crowdsourcing capability is critical to ensure autonomous vehicles have access to

live maps across a wide region.

Driving Policy (Planning, Predicting and Acting)

Once a robust environmental model (sensing + mapping) is achieved, the hard part

begins in some ways. Similar to sensing, the driving policy problem has a number of

approaches from the roots of computer vision/deep-learning, robotics, and new AI

approaches such as reinforcement learning and behavioral/imitation cloning. And

the methods of AV development — from real-world miles to simulation — also tend

to differ between various players.

On the surface, planning, predicting and acting can seem like a straightforward

exercise — know where you need to drive and just get there without hitting

anything. In reality, a simplified approach such as this would in effect optimize

speed at the expense of agility. An AV — particularly an urban RoboTaxi — needs to

be at or near human driving agility (within reason). A lack of agility carries three

detrimental consequences: (1) safety, since overly tentative driving can actually

create accidents; (2) consumer acceptance, if consumers feel like RoboTaxis delay

their arrival at a destination, or if the drive feels unnatural and uncomfortable; and

(3) as a result of that, congestion if RoboTaxi networks compensate for poor agility

by putting more AVs on the road in an attempt to reduce wait times.


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Driving policy can be thought of in three buckets. The first is strategy/tactics in the

planning phase, which is something humans do all the time — knowing which lanes

you need to be in, when/where is ideal to merge into another lane. The second is

the planning of complex negotiations with other road users or even obstacles, which

is something humans also do all the time. Merging in lanes, inching through

pedestrians/cyclists through an intersection, unprotected left turns, deciding whether

to drive over a small obstacle on the road or change lanes, predicting the collective

behavior of other vehicles on the road. The third is vehicle control itself, in effect

how to translate the decisions into safe yet decisive driving actions.

There are numerous approaches today to driving policy and frankly this is where

much of the “secret sauce” for companies sits. Some of those are techniques

include heavy-scenario simulations, reinforcement learning techniques (almost like

a chess game), motion-planning predictive models, and end-to-end behavioral

cloning. These techniques often stem from the robotics field, deep learning, and

other emerging AI techniques such as Waymo’s recently presented Chauffeur Net.

Some of these techniques aren’t heavily debated, such as the value of

reinforcement learning, but others are. For example, the value of real-world miles

versus simulated miles is a common debate among AV experts. Not only in the

context of which is “better”, but also in the recognition that not all “miles” are created

equal. The right answer is probably somewhere in the middle. Real-world miles

need to be complex and probably measured not by “how many miles” per se but by

both the number and complexity of detections/scenarios per mile. Since it’s hard to

simulate for what you don’t know exists in the real-world, the complex real-world

miles can provide valuable training scenarios for simulations to train upon.

Behavioral cloning — or collecting data on human driving to effectively learn to

mimic in an AV — is another approach that attempts to solve the problem from the

other side. While there’s no question of the value of learning human driving

behavior when building an AV driving policy, behavior cloning doesn’t seem robust

enough as a primary method for driving policy. This was a point Waymo recently

made in its Chauffeur Net presentation, which augmented classic imitation learning

by exposing the model to certain perturbations and losses that discourage bad

driving behavior.

The driving policy challenge is perhaps the biggest obstacle standing in the way of

AV commercialization, particularly for urban RoboTaxis. Highway driving still

involves plenty of policy negotiation moves but mostly with other cars, and even

then the AV can be geo-fenced to minimize risk (such as limiting the feature to

middle/left lanes, or not offering automatic lane changes).

The open questions around driving policy techniques today are:

Which methods will work best, since it’s still unclear whether any particular player

has found the “right” solution?

The scalability of different approaches, particularly those that have been

designed to operate in a particular domain (city).


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Figure 78. Select Driving Policy Maneuvers Sorted by Degree of Difficulty (least-to-most

complex)

Vehicle Maneuvers Driving Policy Comments

Single-Lane Highway (middle lane/no lane change)

Detect vehicles cutting-in, understanding context, (detect vehicle turn signals, basic prediction)

All-Highway (all-lanes)

Ability to handle oncoming traffic emerging in the right lane, avoid unwanted exits, negotiate lane-changes, more advanced path-panning

Intersection- 4 Way Stop Signs Negotiate with other vehicles, understand vehicle & human facial cues for take-way & give-way

Cyclists/Scooters Predict behavior while considering local norms of engagement

Intersection- Urban Traffic Lights Negotiate with crossing pedestrians safely, but not too conservatively

Roundabouts Significant amount of vehicle negotiation

Unprotected Left Turns, Complex Intersection

Predicting oncoming traffic & crossing pedestrians

Emergency Vehicles Understanding context (all vehicles moving to another lane), why the emergency exists


ADAS-AV Architecture & Compute

Previous and mostly current ADAS architectures are known as distributed in that

each sensor performs raw data analysis at the sensor itself, and then sends over

the output over the CAN to a central electronic control unit (ECU) for sensor fusion,

if multiple sensors are used. The fusion is performed at the object data level after

the raw data has been processed at each sensor. Fusion at this level is mainly

about resolving sensor disagreements or forming a high-level of confidence to

initiate an automatic emergency braking action. For example, if the sensors don’t

entirely agree the vehicle could alert the driver of possible danger without actuating

the brakes.

As vehicles migrate to level-2+ and full AVs, the architecture is expected to change

to centralized processing whereby the raw data is sent to a central ECU where data

is collected, analyzed, and fused. A good example of this is the Audi zFAS domain

controller found on the Audi A8. The controller processes data individually from the

vision, radar, LiDAR, and sonar sensors and then a central chip calculates the

environmental model that’s also localized with a map. Audi uses chips from

Mobileye (EyeQ3) and NVIDIA for the specialized computing tasks.

Having a deep understanding of the software requirements arguably yield more

efficient chip design, where that design is geared towards the specific software

requirements. This is a point that Mobileye (an Intel Company) has often made, and

one that Tesla also recently discussed as a rationale behind moving to its in-house

Hardware 3.


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Figure 79. Summary of AV Computer Offerings

NVIDIA Competitor 1 Competitor 2 Competitor 3

DRIVE PX Pegasus DRIVE PX Xavier DRIVE PX 2 EyeQ4 EyeQ5 BlueBox HAD System

Process 16nm FinFET 16nm FinFET 16nm FinFET 28nm FD-SOI 7nm FinFET 28nm FD-SOI

SoCs 2x Xavier Xavier 2x Tegra X2 Parker Dual R-CAR H3 SoCs &

RH850/P1H-CMCU

Discrete GPUs 2x Post-Volta N/A 2x Uknown Pascal

CPU Cores 16x NVIDIA Custom ARM

8x NVIDIA Custom ARM

4 NVIDIA Denver & 8x ARM Cortex-A57

8x dual-threaded 64 bit MIPS

Cores

Quad1 GHz ARM Cortex

A53 core+ ARM NEON core platform

ARM Cortex A57/A53 cores

and Renesas IMP-X5 parallel

programming core Imagination Tech

PowerVR

GX6650

GPU Cores 2x Xavier Volta iGPU & 2x Post-

Volta dGPUs

Xavier Volta iGPU (512 CUDA Cores)

2x Parker iGPU & 2x GP104

3D GPU + Dual APEX-2 image processing engine

Imagination Tech Power VR

GX6650

DL TOPS 320 TOPS 30 TOPS N/A 2.5 TOPS 24 TOPS 90 DMIPS

FP 32 TFLOPS N/A N/A 8TFLOPS

TDP 500W 30W 250W 3W 10W 40W


Besides the previously discussed AV software, sensors, and compute/domain

controllers, there are a number of other key components and software required for

building a full AV system at level-4. These would include:

1. Far more advanced power and data distribution throughout the vehicle, also

known as the electrical architecture of a vehicle, which includes connectors.

2. Middleware and operating systems to ensure functional safety.

3. Telematics/OTA with related remote data/cloud and cybersecurity capabilities.

4. Redundant steering and braking systems for level-4 AVs.

5. A more advanced human-machine interface to maximize situational awareness

for the occupants of the vehicle. This is both for safety (in a level-4 highway

application) and comfort. A good example of this today is Tesla’s instrument

cluster which shows the driver key objects being detected by the

camera/radar/ultrasonic sensors. Providing drivers/occupants with clear

situational awareness is critical both for safety and consumer acceptance.

This list above doesn’t necessarily include competitively-driven content such as

highly-contented RoboTaxi seats, nicer interiors, or new materials to prolong vehicle

life.

How is the Industry Approaching Software Development?

Software is of course at the core of both ADAS and AV systems. Software not only

determines system performance, but also factors into system cost, and chosen

sensors, and compute. If your vision software isn’t good enough, you might need

fusion even for the basic ADAS tasks. At an AV level, you might end up sacrificing

agility to maximize speed. If your algorithms aren’t efficient, your computing costs

will rise and your ability to scale might suffer. If your maps aren’t readily updatable,

your system performance will also suffer.

“Vehicle Becoming a Software Defined

Platform”

- Aptiv, Investor Slides


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For basic ADAS up to level-2+ systems, automakers have largely leaned on

suppliers for both software and hardware. This was mostly due to core

competencies of Tier-2s like Mobileye (an Intel Company) having previously

established best-in-class solutions at a reasonable systems cost, with the support of

Tier-1s like Aptiv, Magna, ZF TRW, Mando, Valeo.

Other Tier-1s have also invested in their own vision software and fusion capabilities

to compete, allowing automakers to continue relying on the supply base to drive

down prices. Companies here include Veoneer, Bosch, and Denso.

For AVs, automakers are approaching development decisions somewhat differently.

Some automakers have taken an almost entirely in-house development approach.

Automakers we’d put in this camp include GM, Ford, Honda (by virtue of investing in

GM’s Cruise division), Tesla and Toyota through its prior investment in TRI. We think

this approach is being driven by a number of factors:

1. The sheer business opportunity and strategic importance of AVs compels some

automakers to prefer building in-house capabilities, either entirely or for key

aspects such as driving policy software. Because basic ADAS systems didn’t

require complex, if any, driving policy, this created an opportunity for

automakers (sometimes via M&A) to take a more primary role;

2. AVs, particularly RoboTaxis, are a huge financial undertaking. Some

automakers view their resource base as an advantage over suppliers;

3. The need to integrate AV sensors, software, and controls argues that vertical

integration equals speed, and speed equals a better shot at establishing an

early lead for the network effect. For example, both GM and Ford have

acquired their own LiDAR companies in addition to working with partners, while

other automakers have also strategically partnered (Volvo-Luminar). Given that

LiDAR and compute are the two most expensive parts of an AV, the in-house

approach hopes to create competitive cost advantages through future systems

optimization, something automakers and Tier-1 suppliers are good at.

Other automakers have pursued a partnership approaches both with their own

peers and suppliers. A good example of this is the BMW-FCA-Mobileye partnership

(also includes a number of Tier-1 suppliers) where suppliers are used for the

sensing/environmental modeling side and policy is done jointly. The idea here is that

you are leveraging leading suppliers and the resources of peer automakers to build

the top solution. This approach recognizes the tremendous challenge in AV

development and views cooperation as a strategic advantage to enter the market.

Other automakers are preferring not to partner with peer automakers but still

leverage established ADAS suppliers for the sensing/compute architecture, while

working jointly on fusion/policy. To be sure, this isn’t an either/or.

Many automakers are actually taking a dual approach. Companies like GM and

Audi, in addition to their own AV developments, appear to be continuing to leverage

the supply base for level-2+ and level-3 systems (GM is currently working on next-

gen SuperCruise system called UltraCruise, which appears to be migrating from a

mono camera to a tri-focal front-facing configuration). To us this makes complete

sense both from a risk reduction perspective, a learnings perspective and a

business model perspective. For example, we know that GM is leveraging Cruise

Automation for developing and deploying urban RoboTaxis.

“Autonomous Driving Requires

(Automakers) to Cooperate with Leading

Companies Within the Tech Industry”

- BMW, December 2018


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But if GM were contemplating entering AV Subs, leveraging the supply base would

make sense since the AV Sub problem is distinctly different from RoboTaxis, and

requires far more cost optimization on day-1. This goes back to the need to define

AVs not only by their degree of automation (levels 0-5), but also by the targeted

operating domain.

Of course, at the other end of the spectrum you have tech companies developing

their own software stack solutions—Waymo, Zoox, Aurora, Drive.ai, Comma.ai,

Pronto.ai, Voyage, rideshare companies are some examples. Some of these tech

companies have pursued direct relationships with automakers and suppliers. Others

haven’t. Some are focused on complex domains, others in more specifically

targeted less-complex domains (Voyage in the Florida Villages, Nuro for grocery

delivery) and others appear directly focused on partnering with automakers to serve

their requirements.

Figure 80. Select Autonomous Driving/ Shared Mobility Partnership Review

Announcement Date

Traditional Auto Company

Partnering Company/ Investment

Type of Collaboration Investment Amount

Details

3-Oct-18 GM Honda Partnership $2.75bn • Honda investing $2.75bn in Cruise

• $750mn equity investment (5.7% stake in Cruise)

• $2bn over 12 years

• Post-money Cruise valuation to $14.6bn

31-May-18 FCA Waymo Partnership • Expansion of partnership

• FCA providing 62k additional Pacificas to Waymo fleet

28-Mar-18 Daimler/ BMW 50/50 JV • Merging mobility services business units

• Combining on-demand mobility offering in CarSharing, Ride-Hailing, Parking, Charging, Multimodality

27-Mar-18 JLR Waymo Partnership • I-PACE will become part of Waymo's AV fleet from 2020

• Up to 20k I-PACEs in the first two years of production

7-Jan-18 VW NVIDIA Partnership • VW I.D. Buzz to use NVIDIA DRIVE IX Technology for AI Co-Pilot capabilities

4-Jan-18 VW Aurora Partnership (non-exclusive)

• Integrating Aurora's sensors, hardware, and software

• VW develop EV RoboTaxi service

4-Jan-18 Hyundai Aurora Partnership (non-exclusive)

• Integrating Aurora's sensors, hardware and software

• Hyundai plans to commercialize L4 vehicles by 2021

24-Oct-17 Aptiv nuTonomy Acquisition $450mn • Aptiv acquires nuTonomy for $450mn

10-Oct-17 Magna BMW/Intel/Mobileye Partnership • Magna joins BMW, Intel/Mobileye coalition

9-Oct-17 GM Strobe Acquisition • GM acquires Strobe to reduce LiDAR usage costs

17-Sep-17 Aptiv LeddarTech Partnership • Collaborating to develop low-cost corner LiDAR solution

• Aptiv made minority investment in LeddarTech

18-Aug-17 Aptiv Innoviz Partnership • Collaborating to develop low-cost corner LiDAR solution

• Aptiv made minority investment in Innoviz

16-Aug-17 FCA BMW/Intel/Mobileye Partnership • FCA joins BMW, Intel/ Mobileye coalition

26-Jun-17 Zenuity NVIDIA Partnership • Develop systems that use AI to:

- Recognize objects around vehicles

- Anticipate threats

- Navigate safely

16-May-17 Aptiv BMW/Intel/Mobileye Partnership • Aptiv joined BMW, Intel/ Mobileye for developing AVs

10-May-17 Toyota NVIDIA Partnership • NVIDIA will deliver AI hardware and software tech enhancing autonomous driving system capabilities

13-Mar-17 Mobileye Intel Acquisition $15.3bn • Intel buys Mobileye for $15.3bn

10-Feb-17 Ford Argo AI Investment $1bn • Ford investing $1bn over 5 years in Argo AI

• Develop a virtual driver system for the Ford's L4 autonomous vehicle coming in 2021



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Profile of Major Automakers Based on publically available information, we have updated our timelines for select

automakers and mobility players. The list is naturally fluid as not all automakers

have likely disclosed plans, so it should be read as our best view at this point in

time.

Detroit 3 (“D3”)

General Motors appears the most aggressive in its pursuit of both an urban

RoboTaxi AV network (under Cruise) as well as increasing level-3 and level-4

features. GM is expected to commercialize an urban RoboTaxi rideshare network as

early as 2019, widely expected to be in San Francisco. From there, we expect the

company to attempt to rapidly scale in order to create a network effect.

Concurrently, we expect GM to launch the next-gen version of SuperCruise around

2020-21, a system that management internally refers to as “UltraCruise”. Though

not much is known about UltraCruise’s capabilities, we assume it to be a level-3 or

even level-4 highway system expanding on the level-2+ SuperCruise. One possible

hint on UltraCruise came from a December 2018 unconfirmed “spy shot” (from

Autoblog) of a 2020 Cadillac Escalade that appeared to show a tri-focal camera

behind the windshield. GM’s plans to more broadly adopt this technology across its

vehicles suggest management confidence at the capability and appeal of this next-

gen feature. From there it’s less clear where GM expects to take its non-RoboTaxi

AV-platform. To us, the natural progression would be to leverage “UltraCruise” and

Cruise Automation’s RoboTaxi AV tech to launch AV Subs. GM has natural

advantages by virtue of having a large dealer network and a wide offering of

different vehicles, both of which could become competitive advantages. There are

perhaps a few early signs that AV Subs might be in GM’s future as Cadillac has

previously experimented with subscription-based cars.

– Strengths: (1) lead in urban RoboTaxi AV development and level-2+

technology (SuperCruise); (2) ability to design/build purpose-built AVs (as

opposed to retrofits); (3) Maven peer-to-peer platform; (4) EVs offerings in the

U.S. and aggressive EV product plans; (5) plans to meaningfully upgrade

SuperCruise (to “UltraCruise) in 2020+; (6) wide dealer network/vehicle

offerings for AV Subs.

– Weakness: Lack of OTA in personally-owned vehicles today

Figure 81. GM’s Path Towards Autonomous

Source: Company Reports. Citi Research

Ford continues to develop an AV RoboTaxi service for 2021 deployment, with a

focus on ridesharing as well as deliveries. Ford’s AV will be a purpose-built hybrid

vehicle and not sold to consumers. After its investment and partnership with Argo AI

in 2017, Ford began testing AVs in Miami including with partners such as Postmates

and Domino’s Pizza.

General Motors

2016 2017 2018 2019 2020 2025 2030

L2+ Supercruise (Highway)

2021

L4 RoboTaxi(Cruise AV Network)

L3+ UltraCruise


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When we visited Ford’s AV team in Miami in November 2018, we were impressed

by the smoothness of the Ford-Argo AI AV ride, which handled several complex

scenarios with confidence including pickups and drop-offs utilizing Ford’s rideshare

app. The vehicle felt properly optimized for both safety and agility. The one

disengagement we experienced occurred during an unprotected left turn facing a

construction site — a challenging maneuver for any AV. Still, the ride and our

discussions with Argo management made us feel more confident that Ford is on a

path to commercialize AVs in 2021. With the exception of fog, Argo noted the

remaining to-do list entails solvable problems that just take time. On its personally-

owned vehicles, Ford has taken a somewhat less aggressive approach to semi-

autonomous features. The company seems to favor a level-2 approach as opposed

to level-2+, the distinction being that level-2 systems require hands on the wheel

(an adaptive cruise control plus lane centering system), whereas level-2+ allows for

the driver to be hands-free in certain environments. Ford has suggested that it will

skip level-3 due to concerns over the human-machine handoff problem. A level-4

plan for personally-owned vehicles has not been specifically articulated beyond the

general timeframe of ~2025, but we do believe that AV Subs would be attractive for

Ford given the company’s expansive dealer network and vehicle offering range.

– Strengths: (1) ability to design/build purpose-built AVs (as opposed to

retrofits); (2) Argo AI team and AV test vehicles impressed us in our Nov’18

test drives; (3) wide dealer network/vehicle offerings for AV Subs.

– Weakness: (1) Lack of OTA in personally-owned vehicles; (2) the argument

that 2021 marks a later U.S. launch than some competitors, which risks an

early-mover advantage in a market Ford agrees is likely to be ‘few-winners-

take-all’; (3) the argument that Ford’s slower push for level-2+ and level-3

might negatively affect its future technology position for level-4 on personal

vehicles; (4) EV presence not as strong as peers.

Figure 82. Ford’s Path Towards Autonomous

Source: Citi Research, Company Reports

Fiat-Chrysler has taken a somewhat different approach that is focused on

partnerships to attain various levels of automation. FCA aims to launch level 2+/3

systems in the 2019-2021 timeframe. This includes relationships with Tier-1

suppliers like Aptiv as well as consortiums like BMW-Mobileye, Aptiv, and Magna.

With Aptiv, FCA targets level-2+ around 2020. With the BMW-led consortium, FCA

targets level-3 highway around the 2021 timeframe. For personally-owned vehicles,

FCA believes full autonomy will be achieved by of 2023. On the RoboTaxi AV side,

FCA has taken a manufacturer approach by partnering with Waymo to deliver

Pacifica hybrid minivans. FCA expects to deliver nearly 63k Pacifica units to Waymo

in 2021. FCA and Waymo have also been in discussions for equipping Waymo

systems for FCA retail customers. This will be an interesting development to keep

an eye on particularly if FCA decides to enter the AV Subs market around 2023,

when the company views full autonomy for personally-owned vehicles as reachable.

Ford Motor Company

2016 2017 2018 2019 2020 2021 2025

L2 Co-PilotL4 (Rideshare + Delivery)

2030


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retrofits); (2) early involvement with a wide variety of key partners including

close ties to Waymo (vs. competing OEMs); (3) wide dealer network/vehicle

offerings for AV Subs.

– Weakness: (1) lack of OTA in personally-owned vehicles; (2) RoboTaxi

involvement appears confined to contract manufacturing; (3) EV presence not

as strong as peers.

Figure 83. FCA’s Path Towards Autonomous


Japan 3 (J3)

Nissan has outlined a fairly clear path towards autonomous driving and has

generally taken an aggressive approach, particularly with level-2 and level-3. In

2017 Nissan launched a level-2 system called ProPilot in Japan, and has since

expanded the feature to the U.S. and Europe. ProPilot is a single-lane highway

level-2 system operating on a single mono camera powered by Mobileye’s EyeQ3

chip, with ZF-TRW as the Tier-1 supplier. Nissan was also one of three automakers

to begin harvesting data using Mobileye’s REM crowdsourced mapping solution.

Nissan is expected to move into level-3 automation including highway/multiple lane

deployment in 2018 (using Mobileye EyeQ4) and then urban roads/intersections by

2020. With regard to RoboTaxis, Nissan does have plans to deploy in Japan around

2022. Nissan has shown the future fully autonomous concept electric vehicle

capable of level 4/5 autonomy with a two-mode interior system that toggles between

driving modes and uses advanced heads up displays.


retrofits), (2) aggressive level-2/level-3 deployment; (3) map data harvesting

(REM); (4) strong EV presence.

– Weakness: (1) lack of OTA; (2) Nissan’s RoboTaxi target launch year (2022)

is later some of its peers.

Figure 84. Nissan’s Path Towards Autonomous


Fiat Chrysler Automobiles

2016 2017 2018 2019 2020 2021 2025

L2+

2030

L4 - Test Vehicles for Waymo (63k by 2021)

L3 Highway )

Personal Vehicle (2023)

Nissan

2016 2017 2018 2019 2020 2021 2025 2030

L2 L3 (REM)L4 RoboTaxi (Japan)

(2022)


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Honda was probably the least vocal automaker on AVs for several years, until

October 2018 when the company entered into a strategic investment/partnership

with Cruise, GM’s majority-owned AV division. Prior to this investment, Honda’s role

in the RoboTaxi AV space wasn’t clear. Post the investment, we believe Honda will

likely attempt to leverage the Cruise AV technology and a future jointly-developed

GM-Honda-Cruise purpose-built AV, in order to enter Japan. Our assumption is that

this is probably a ~2021 event as it could take two to three years for the Honda-GM

joint venture AV vehicle to make it to production (a typical range for time to market).

On the personally-owned vehicle side, Honda is expected to launch level-3 highway

features around 2020 (Honda appears to be one of Mobileye’s level-3 customers in

the 2019+ timeframe). For level-4, Honda is targeting ~2025 for personally-owned

cars. It is unclear whether Honda has ambitions to launch AV Subs networks, AV

Features, or both.


retrofits); (2) investment in Cruise-AV.

– Weakness: (1) lack of OTA; (2) level 2+/level-3 deployments appears to be

somewhat later than peers, as does the 2025 timeline for level-4 on personal

cars; (3) EV position not as strong as peers

Figure 85. Honda’s Path Towards Autonomous


Toyota has been expanding the Toyota Research Institute in-house R&D division.

The company deployed a level-2 system called Lexus CoDrive in 2017, an

adaptive-cruise-control with lane keeping. Toyota has previously set its sights on a

2020 feature called Highway Teammate which appears to us to be a level-3/level-4

highway driving feature. On the RoboTaxi front, Toyota’s perhaps most significant

move came in August 2018 when the company invested $500 million in Uber and

agreed to integrate Uber’s AV technology into the Toyota Guardian for purpose-built

vehicles that will be deployed on Uber’s rideshare network. Toyota plans pilot scale

deployments on the Uber network starting in 2021.


retrofits); (2) partnership with Uber.

– Weakness: (1) lack of OTA; (2) level 2+/level-3 deployments appears to be

somewhat later than peers; (3) EV position not as strong as peers; (4) Uber

partnership might suggest internal level-4 AV capabilities weren’t as strong.

Honda Motor Company

20302016 2017 2018 2019 2020 2021 2025

Investor in Cruise (GM)

L3 (Highway)

L4 (personal cars)


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Figure 86. Toyota’s Path Towards Autonomous


German 3 (“G3”)

BMW has taken a collaborative approach to AVs by creating a consortium of

automakers and suppliers. That consortium, initially formed in July 2016, today

consists of BMW, FCA, Tier-2 Mobileye (Intel) and Tier-1s Aptiv, Magna, and

another supplier. In semi-autonomous, BMW has also taken an aggressive

approach by deploying level-2, level-2+ (in the U.S. with hands-free option),

crowdsourced mapping and, starting in 2020-21, level 3 highway driving features for

up to 130km/h speeds. BMW’s latest level-2+ offerings feature a tri-focal camera

operating on the Mobileye EyeQ4 chip along with radar and DMS. As for level-4,

BMW continues to target 2021 but for pilot urban fleets in several cities worldwide. It

appears for now that this deployment would still in the advanced testing phase as

opposed to a driverless commercial service.

– Strengths: (1) collaborative approach to AVs could provide an advantage

particularly as it relates to scaling; (2) an ability to design/build purpose-built

AV; (3) strengthening EV position; (4) crowdsourced mapping; (5) active

deployment of level-2+ features; (6) Daimler-BMW mobility partnership

– Weakness: (1) lack of OTA relative to peers like Tesla; (2) BMW’s network

strategy (RoboTaxi and AV Subs) still isn’t entirely clear. Though BMW does

have the mobility assets to pursue RoboTaxi services, and we do believe the

company has those intentions (likely in Europe), the AV roadmap still seems to

more emphasize highway features, which we view as less exciting relative to

the broad AV network opportunity; (3) the 2021 timeframe for urban AV pilots

puts BMW at similar timetables as peers, and behind a few other players

planning to deploy more quickly.

Figure 87. BMW’s Path Towards Autonomous


Daimler has pursued both internal development and partnerships for various levels

of autonomy. On the RoboTaxi side, Daimler has partnered with Tier-1 supplier

Bosch to launch in an urban environment “early in the next decade”. To that, in the

second half of 2019, Daimler is expected to start piloting in San Jose, California. On

the personally-owned vehicles, Daimler offers level-2 features called Distronic Plus

with Steering Assist as well as Drive Pilot. The company plans to launch alLevel-3

system around 2020 on the S-Class.

Toyota Motor Corporation

2016 2017 2018 2019 2020 2021 2025

L3/4 (Highway)

2030

Uber pilotL2

BMW

2016 2017 2018 2019 2020 2021 2025

L2 L2+ (U.S.) L4 Urban Pilot

2030

L3


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– Strengths: (1) ability to design/build purpose-built AV; (2) strengthening EV

position’; (3) partnership with reputable Tier-1 for RoboTaxi development; (4)

Daimler-BMW mobility partnership

– Weakness: (1) Lack of OTA relative to peers like Tesla; (2) RoboTaxi launch

schedule appears later than peers, as does level-3 deployment.

Figure 88. Daimler’s Path Towards Autonomous


Volkswagen/Audi has taken a more aggressive plan in recent years both towards

semi-autonomous features as well as urban RoboTaxi services (MOIA). The

company has taken several approaches including advanced programs with

suppliers (Aptiv/Mobileye others), internal AV development, and partnerships with

startups like Aurora. VW plans to launch its MOIA shuttle in Hamburg at the end of

2018 with a fleet that will expand to 200 in the first phase. The company aims to

launch an urban AV shuttle in the 2021+ timeframe, along wither personal

autonomous vehicles (highway piloting under the Audi brand at level-3 around 2023

and level-4 hub-to-hub around 2024-2025). Separately, in October 2018 VW

announced that it would partner with Mobileye (Intel) and Champion Motors to

commercialize a level-4 mobility-as-a-service operation in Israel that will begin

development in early 2019 and roll out in phases with full commercialization in

2022. The service is expected to start with several dozen AVs (all EV) and grow into

the hundreds.

– Strengths: (1) strong partnerships, including with AID who is targeting

RoboTaxi deployments in 2021; (2) rapid timeline for RoboTaxi piloting and

deployment; (3) ability to design/manufacturer purpose-built AV; (4) increasing

strength in EVs

– Weakness: (1) lack of OTA relative to peers like Tesla; (2) level-3 deployment

has felt more constrained than originally thought.

Figure 89. VW’s Path Towards Autonomous


Volvo has historically been very active on ADAS and semi-autonomous systems,

including the level-2 Pilot Assist II feature leveraging a mono/radar sensing

configuration (Mobileye/Aptiv). In recent years Volvo has forged new partnerships

with Veoneer and a co-owned software arm, Zenuity. NVIDIA has also become

more prominent as the compute provider. Volvo has discussed plans for a 2021

personal level-4 highway feature. On the RoboTaxi front, in 2016 Volvo partnered

with Uber to provide the rideshare company with XC90 SUVs that Uber retrofitted

Daimler AG

2016 2017 2018 2019 2020 2021 2025

----------L4 (robotaxi)-----

2030

L2 L3

Volkswagen AG / Audi

20302016 2017 2018 2019 2020 2021 2025

L4 RoboTaxi (MOIA Europe, Israel Pilots)L3

L4 Highway, L4 City TJA (2024)


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100

with its AV sensing/compute. Uber’s recent partnership with Toyota does, however,

suggest that the Volvo-Uber relationship might not expand beyond the XC90 test

fleet. In 2018 Volvo presented the 360c concept, a vision for future AV/EV travel

with a sleeping environment, mobile office, and entertainment spaces. The concept

appears to reflect a view of future RoboTaxi travel, though it’s unclear whether Volvo

will also look at operate a RoboTaxi network.

– Strengths: (1) ability to design/manufacturer purpose-built AV; (2) strong

technical know-how in ADAS and level-2; (3) potential advantages from

Zenuity ties; (4) strong partnership with Luminar, who appears to have made

progress in LiDAR detection capabilities at long-range.

– Weakness: (a) lack of OTA relative to peers like Tesla; (2) questions whether

supplier shift might slow down level-3/level-4 deployment.

Tesla Case Study

Ironically, of all the things Tesla got right in the original Model S launch, ADAS was

not one of them. In fact, the original Model S wasn’t equipped with any ADAS

systems including blind-spot detection. Tesla realized this disadvantage fairly

quickly and proceeded not only to catch up but to attempt to leapfrog in the industry

in this area.

Until this day, Tesla’s approach carries a mix of controversy, praise, and opportunity

— at times mixed with some confusion or misreporting about Tesla’s actual

capabilities, advantages, and disadvantages.

The first iteration of Tesla’s ADAS suite, known as Autopilot 1.0, was equipped with

a front-facing mono camera, one front-facing radar, and 12 ultrasonic short-range

sensors around the vehicle. Tesla was the first automaker at the time to deploy the

Mobileye (now an Intel Company) EyeQ3 chip. The uniqueness of Tesla’s approach

was to design the EyeQ3 into a complete system entirely in-house as opposed to

using a Tier-1 supplier. Autopilot 1.0 was effectively an ADAS + level-2 system,

enabling automatic emergency braking, lane detection/keep, and semi-autonomous

driving.

Seemingly overnight, Autopilot 1.0 thrust Tesla from an ADAS laggard to a leader in

ADAS/level-2 semi-autonomous driving, albeit still with a lack of robust blind-spot

detection as compared with radar-based systems that were readily available on

many cars. Nonetheless, the forward-facing Autopilot features were impressive,

particularly for the speed by which Tesla was able to launch them on the EyeQ3

(Audi was the second launch months later).

However, Autopilot ultimately became controversial after a number of vehicle

crashes were attributed to the system, including fatal ones. The biggest flaws, as

we saw them, were a lack of driver-monitoring systems (DMS), a lack of any geo-

fencing (i.e. highway only or divided highway only), and a lack of effective driver

education about the capabilities and limitations of the system. Yes, it is the drivers

responsibility to ensure safety, but a driver might not know that the system cannot

detect a red traffic signal (or a tire on the road) until it’s too late. To be sure, this

problem isn’t exclusive to Tesla, but Tesla’s human-machine interface arguably

inflated this risk. In fact, the controversy around Autopilot incidents culminated in a

split between Mobileye and Tesla, with Tesla moving on to design its own in-house

system, called Autopilot 2.0/2.5, and Mobileye continuing to grow its EyeQ chips on

some of the most advanced level 2-3 systems offered today (GM SuperCruise, Audi

A8, BMW tri-focal on X5, and other vehicles).


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In the fourth quarter of 2016, Tesla launched Autopilot 2.0 and months later a

slightly improved 2.5 hardware version reportedly featuring an improved radar and

occupant monitoring camera (DMS system, albeit not yet operational to our

knowledge and with the placement of the camera at the rear view mirror as opposed

to the instrument cluster area).

Tesla’s new sensing suite consisted, and still consists today, of 8 surround cameras,

1 forward-facing radar, and 12 upgraded ultrasonic sensors. The camera-heavy

system relied on a “tri-focal” frontal configuration with a narrow field-of-view (FOV)

long range camera, a mid-range camera, and a wide FOV short-range camera. In

addition to the three forward cameras, Autopilot 2.0/2.5 was equipped with side-

forward cameras (100m range) and side-rear cameras. The front-radar provides a

250m long-range detection.

Utilizing the new hardware suite, Tesla began developing and utilizing its own neural

net software on NVIDIA hardware. The road to catch-up to Autopilot 1.0 capabilities

(through OTA updates) took longer than Tesla initially expected. Though Autopilot

capabilities showed gradual improvement (particularly lane-detection), the system

still lacked certain ADAS functionality (pedestrian detection) that was becoming

more common on competing systems. The interpretation was that either Tesla was

not collecting as much shadow miles as some believed, and/or the hurdle to

achieve 99.99% software accuracy proved more difficult than Tesla believed, even

with the advantage of this data collection. Another interpretation was the Tesla

neural networks required more computing resources than initially thought, a theory

that was supported by Tesla’s announcement in 2018 that it would shift to an

internally developed chip (“hardware 3”) during the first-half 2019. Related to that,

some evidence emerged that Tesla’s software approach had pivoted since the

Mobileye split. When that split first occurred, it was thought that Tesla would attempt

to run an end-to-end neural network (“software 2.0”) feeding massive amounts of

video data to produce driving outputs (steering, accelerating/braking). More recent

evidence suggests that Tesla has moved a bit more towards the classical sensing

approach where images are annotated and the network is trained to detect

individual objects (motorcycle, police car).

Ahead of the hardware 3 upgrade, in the fall of 2018 Tesla rolled out its next-

generation Autopilot software stack (V9) on the 2.0/2.5 sensing suites. The V9

update opened all 8 cameras and expanded the neural net detection to apparently

include vehicles at various angles, some degree of pedestrian detection, some

degree of free space and, importantly, blind spot detection thanks to the expanded

camera coverage. From a sensing and human-machine interface (HMI) perspective,

V9 was a step-function improvement versus the prior software stack, but still lacked

a functioning DMS system, detections for traffic lights and traffic signs (road barriers

possibly too), as well as HD mapping to augment on board sensors. Some of these

detections are expected to be introduced in 2019 under Tesla’s new Hardware 3

compute platform. Assuming Tesla can achieve this, its sensing capabilities should

be able to match or even exceed competing systems. However, the lack of sensor

redundancy will remain an issue — while Tesla could possibly upgrade its existing

sensors, adding surround radars and/or LiDAR could prove more difficult as a

retrofit. So once Tesla launches its Hardware 3 compute in 2019, it will be

interesting to see whether Tesla decides to upgrade Autopilot 2.0/2.5 sensors or

introduce an entirely new Autopilot 3.0 sensing suite perhaps with greater sensing

redundancy. It will also be interesting to see whether Hardware 3 allows Tesla to

unlock the apparent DMS system located in the rearview mirror.

For driver policy, Tesla’s software approach appears to rely on real-world vehicle

data collection to build behavioral cloning or imitation learning models — effectively


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learning how to drive from humans (Tesla customers). This is a method that has

both pros and cons to it. Pros include that humans often learn from watching other

humans, so doing this on massive quantities of data can vastly improve AV

safety/performance. A lot of this can also be done in shadow mode.

Cons include a difficulty in pinpointing the origin of software errors, learning from

“bad” drivers and the inability to dissect why a human made a particular decision,

which might lead to imperfect training if the vehicle sensing input doesn’t match the

human’s input in a particular scene. Waymo’s recent Chauffeur Net paper shined a

light on this problem, as Waymo found that pure imitation learning on 30 million

examples was insufficient to adequately train an AV, partially because of the issue of

not knowing why a driver behaved the way they did. Without knowing the “why”, it’s

hard to make the correct systems improvement. The other issue is that Tesla can’t

control where miles are collected — repeat routes eventually lose their analytical

value, and analyzing disengagements is tough because you simply don’t know why

a driver disengaged, which could yield false learnings.

We regard Autopilot in its current form as still a level-2+ highway autonomous

feature. For highway driving, driving policy is less complex than an urban

environment with right/left turns and pedestrians crossing. Clearly, Tesla’s focus on

vehicle and lane detection over traffic light/sign detection is partly a function of the

intended use case being highways mainly.

What About LiDAR?

Tesla’s sensing suite is known as the one who didn’t pick LiDAR. Part of the

decision, we believe, relates to the use-case discussion that we delve into below.

For example, we do not believe Tesla is aiming to launch urban RoboTaxis, which

partly explains why LiDAR wasn’t chosen. Rather, we think Tesla’s AV aspirations

are more aligned with our AV Sub concept, so from that perspective Tesla’s sensing

suite can be viewed as a competitive choice aimed at establishing the lowest cost

AV system with the highest amounts of usable data (OTA) and with an early mover

advantage with a popular EV. We have always been big fans of the capabilities of

vision and Mobileye’s current AV test fleet in Israel (soon expanding to California) is

demonstrating significant capabilities with effectively a vision-only configuration at

the moment. So in principle we don’t disagree with Tesla’s view that vision can take

on significant primarily sensing roles in certain domains like AV Subs. That being

said, even those vested in vision acknowledge the need for redundancy, and Tesla’s

system still appears to lack general redundancies that many leading vision

companies — including Mobileye — believe is required for level-4. That includes

both LiDAR and radar redundancies for weather, lighting conditions, superior free

space detection, and functional safety. The end result, in our view, isn’t that Tesla

“can’t” get to level-4 but that its domains could prove more restrictive. For Tesla this

is a capability versus cost equation. The AP2.0/2.5 hardware clearly has a cost

advantage over more redundant systems, but the question is whether the presumed

domain limitations will come at a significant cost to Tesla in the AV race. In other

words, is AP 2.0/2.5 even good enough to achieve a stage-1 AV Sub model? The

answer isn’t clear at the moment but right now we’d say probably not. This is

particularly true as we continue to see sensing improvements in both radar and

LiDAR. Now, Tesla can of course upgrade its sensing-suite with hardware 3.0, but

the company might then have to contend with customer/legal pushback on having

sold many vehicles with the promise of full autonomy on Autopilot 2.0/2.5 hardware.

This will be a very interesting storyline to follow in 2019 as Tesla looks to upgrade to

its internally-developed Hardware 3 chip.

.


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What Tesla’s Position for AVs?

Let’s go back to the three basic AV use-case pillars we see emerging over the next

10 years:

1. Urban RoboTaxi – We don’t view Tesla as a player in urban RoboTaxis, for a

number of reasons. First, as mentioned we don’t view Tesla’s AP sensor suite

as robust enough for the complex urban market. Second, Tesla hasn’t, to our

knowledge, done dedicated urban AV testing, which is critical to deploying in

cities, in our view. Third, the RoboTaxi market requires significant capital

outlays and is inherently low-volume — if Tesla is looking to sell as many EVs

as possible, focusing resources on RoboTaxis arguably doesn’t fit the

company’s mission. So we would strongly challenge the notion that Tesla is a

RoboTaxi player alongside Waymo, GM-Cruise, Zoox and others who are

actively testing RoboTaxis in urban domains. Yet, that shouldn’t be taken as a

bearish call on Tesla’s broader AV position. In fact, we think Tesla’s approach

was novel in the context of what we view as an attempt to build something akin

to an AV Subscription network, or the Tesla Network as the company

occasionally defines it.

2. AV Subs – This actually makes a lot more sense for Tesla’s stated mission. An

effective AV Sub model can drive higher EV penetration and make Tesla’s

vehicles far more competitive than peers, mainly because of the Tesla

AP2.0/2.5 installed base that’s backed by OTA updates. If Tesla is looking to

promote EV adoption while protecting its share, AV Subs offers a far better path

to accomplish this versus building purpose-built and dedicated urban RoboTaxi

fleets. Indeed, we think the Tesla Network had this sort of business model in

mind. But here too the jury is still out whether Tesla’s approach was and is the

right one, per our analysis above. On the one hand, Tesla’s decision to limit

sensor redundancy might prove wise as a means of establishing significant

cost and scale advantages. On the other hand, Tesla’s seemingly slow

progress with internal neural net development (since AP2.0 was installed) could

allow competitors to catch-up, or for competitors to gain an edge on Tesla by

using next-gen sensors such as imaging radars. In other words, Tesla arguably

boxed itself in by establishing a large installed-base on vehicles sold with the

promise of eventual level-4/level-5.

3. AV Features – This is clearly an area Tesla has been focused on with the

launch of Autopilot and then Enhanced Autopilot. Here too Tesla’s OTA

advantage stands out, as the AP2.0/2.5 hardware sets have already seen

significant software upgrades since inception. As a feature, Autopilot stands out

as being relatively less restrictive in terms of where consumers can turn the

feature on. This fact isn’t without controversy though as the driver is arguably

left with the responsibility of knowing what the sensors can and cannot detect

— for example knowing that Autopilot won’t (presently) detect a red light.

Tesla’s UI also appears to be the most advanced as the all-digital instrumental

cluster provides the driver with robust situational awareness of surrounding

vehicles and lanes. One piece of hardware where Tesla’s leadership is less

apparent is driver-monitoring-systems (DMS). Over the past year or so, DMS

has increasingly become an industry standard for level-2+ systems — GM

SuperCruise has it as do some of the newer systems from luxury European

automakers. Tesla’s position here is a bit mysterious as AP2.5 seems to have

an occupant monitoring camera embedded in the rear view mirror. To our

knowledge, that camera isn’t currently operating as part of the Autopilot feature.


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Korean Autos: Where They Stand in Autonomous Driving Long-Term Megatrend? Korean automakers/suppliers are generally perceived as “fast followers” in the auto

megatrend (i.e. xEV, autonomous driving), which we think is a fair statement. Until

now, Hyundai Motor Group (HMG) has focused more on “in-house” development of

future technologies, but the group appears to have shifted its strategy to more

“open-innovation” based on an increasing number of collaborations with external

parties including direct investment in leading auto start-up players. In the

autonomous driving scene, HMG aims to deliver “level-4 and level-5 (or fully

autonomous driving)” commercialization by 2021 and 2030, respectively. Among

auto parts suppliers, Hyundai Mobis and Mando are likely to be two key suppliers

for HMG’s increasing autonomous driving technology development.

OEMs (Hyundai Motor/ Kia Motors)

Hyundai Motor Group’s vision for future mobility consists of “Clean Mobility”,

“Freedom in Mobility”, and “Connected Mobility”. The “Clean Mobility” initiative can

be summarized as (1) achieving 25% fuel-efficiency improvement on average by

2020 by refreshing 70% of current powertrains; and (2) increasing xEV model line-

ups to 31 of 38 green cars by 2020/ 25 with a goal of being the second largest xEV

producer). To achieve its “Freedom in Mobility” and “Connected Mobility” long-term

initiatives, the group has increasingly expanded its R&D and direct investment on

autonomous driving/shared mobility initiatives in the past years.

For its “Freedom in Mobility” initiative, HMG has developed technologies under the

philosophy of “providing ultimate safety not only to the driver but also to the

passengers/pedestrians/other drivers, by having the vehicle proactively analyze

driving environments and assist the driver when necessary”. In terms of timing for

these higher-level autonomous driving technologies, HMG aims at “level-4

autonomous driving in smart-cities by 2021 and fully-autonomous driving by 2030”.

HMG has successfully commercialized “level-2 autonomous driving technologies:

Partial Automation” such as highway driving assist I & II (HDA) and traffic jam

assistance (TJA) and the group announced autonomous emergency braking (AEB)

will be a standard feature for all new vehicles from 2019.

HMG’s “Freedom in Mobility” and

“Connected Mobility” initiatives: Level-4/ 5

autonomous technology by 2021/ 2030


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Figure 90. Hyundai Motor Group – Autonomous Driving Technology Roadmap

Source: Hyundai Motor, Citi Research

In order to pursue more efficient R&D on autonomous driving technologies, Hyundai

Motor Group established an independent “autonomous driving” technology center in

2017. Its ultimate goal is the development of a “universal” fully-autonomous driving

platform which is able to operating under any driving circumstance (not only in the

perfect world such as “Smart Cities”) through the development/upgrading of existing

ADAS technologies such a smart cruise control (SCC), lane-departure warning

systems (LDWS) and highway driving assistance (HDA). Hyundai Motor Group is

targeting the commercialization of “level-4” autonomous driving in the smart-city by

2021 and fully autonomous driving “everywhere” by 2030. The group believes a

“universal” autonomous driving platform would have advantages by allowing greater

flexibilities in parts-sourcing and delivering cost-savings via greater degree of

“modulizations” (as well as benefiting suppliers).

In addition to the development of a “universal” autonomous driving platform, another

key initiative for the group’s autonomous driving development is “open innovation”.

In addition to its main tech center in Seoul, the group has expanded its global R&D

footprint in Beijing (AI, ICT cooperation), Berlin (Smart City, Mobility Solution), Tel

Aviv (Start-up investment), and Silicon Valley (Innovation Cradle). “Open

Innovation” basically means the group is increasing cooperation or collaboration

with global leading players (e.g., co-developing autonomous driving technology with

Mobileye in July 2017, developing level-4 urban autonomous driving technology in

“Smart-Cities” with U.S.-based start-up company, Aurora in January 2018), as well

as investing directly in emerging players (e.g., investments in U.S.-based self-

driving car radar/ AI start-up, Metawave in May 2018 and U.S.-based AI start-up

Perceptiveautomata in October 2018).

Highway AD Commercialize

Highway &Downtown Smart City

CompleteLevel 4

Partial Level 4

PartialLevel 4

Level 3

Level 1&

Level 2

CommercializeEverywhere

Past Current ~2020 ~2021 ~2030

Key initiative 1): development of universal

autonomous driving platform

Key initiative 2): “Open Innovation” strategy

to increase cooperation with global leading

players and direct investment to


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During the Pyeongchang Winter Olympics in February 2018, Hyundai Motor

successfully demonstrated highway driving assistance (HDA) via its premium

Genesis G80 sedan and fuel-cell vehicle NEXO, in both day and night trials in

seven tunnels which cannot receive GPS signal (including 2 toll-gates and 2

interchanges). It was the first long-haul (200km) demonstration of level-4

autonomous driving technology by the group. Further, HMG commercialized

“navigation-based” smart cruise control (NSCC) to its recently introduced K9 sedan

(Kia) in mid-2018, which enables semi-autonomous driving in non-highways as well.

Hyundai Motor Group successfully demonstrated “level-3” autonomous driving for

large commercial vehicle in 2018.

Figure 91. Hyundai Motor Group: Current Status of Autonomous Driving Technology


Figure 92. Hyundai Motor: Global Footprint for “Open Innovation”


Key milestone of HMG in self-driving car

technology development


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Figure 93. Hyundai Motor Group: “Freedom in Mobility” Roadmap

Source: Hyundai Motor

Suppliers (Hyundai Mobis/ Mando)

Hyundai Mobis and Mando are two key suppliers of ADAS/ autonomous driving

parts to Korean auto manufacturers (Hyundai/ Kia), and we view both companies as

not only continuing to remain key “autonomous driving” part suppliers to Korean

OEMs, but also see increasing opportunities with non-Korean OEMs in the future,

based on their level of technology, well-proven track records, and pricing

advantages versus global peers. Both Mobis and Mando are poised to further

expand investment in R&D, while also expanding collaboration or direct investment

into external players (including start-up companies). Currently, ADAS/ autonomous

car-related revenue accounts for only a fraction of total revenues at both companies

(2% of core parts revenues at Mobis and 9% of revenues at Mando) but we project

growth in ADAS/ autonomous driving parts revenue in both companies of 18-23% to

2022E.

Mobis: Potential to be a Leading Player in Autonomous Scene

Hyundai Mobis was a late comer in the industry but has increased its investment on

ADAS/ autonomous driving technology since 2009 when it acquired Hyundai

Autonet to centralize the group’s investment/development in “Mechatronics”

autonomous driving technologies. Its R&D investment has notably increased in the

past decade (2010: 4.1% of core-parts revenue, 2017: 7.2% of core-parts revenue)

under an integrated R&D function, and Mobis plans to increase R&D investment

further to 10% of core-parts revenue by 2021. ADAS/autonomous driving

technology is a key investment focus for Mobis and the number of

ADAS/autonomous dedicated R&D staff at the company is expected to increase to

1,000+ from the current 600 level.

ADAS/ Autonomous driving parts: key

growth driver (+23%/ +18% CAGR by

2022E) for Mobis/ Mando


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Notable milestones in autonomous driving technology development at Mobis

include: (1) completion of radar (front, front/ rear corner) applicable to level-2 & 3

autonomous driving, which is currently on the marketing progress to HMC/ Kia and

global OEMs, (2) completion of a full-redundancy braking system and motor driven

power steering (MDPS), (3) commercialization of Highway Driving Assistance

phase-I (applied to the Genesis brand) and development of Highway Driving

Assistance phase-II, which will be ready for commercialization (technology

development completed in 2017) into 2019 and ready for commercialization on the

upcoming Genesis-brand models G80, GV80, and GV70. In 2017, Mobis completed

the construction of a test-driving complex (including autonomous-driving test roads)

which will be used as a “cradle” of new technologies including ADAS/ autonomous

driving.

Hyundai Mobis has two phases in their autonomous driving development roadmap:

Phase-1 (Establishing full ADAS sensor portfolio by 2021): The key initiative

by 2021 is internalizing key technologies of radar, front-camera, and LiDAR

through partnerships via collaborations with external partners. For radar, Mobis is

collaborating with global specialists such as SMS (for entry MRR/ high-resolution

SRR) and Astyx (high-end MRR). For cameras, Mobis is currently using both a

Mobileye-developed model and in-house model, but it aims to use a deep

learning-based in-house model for level-4 autonomous technology by 2025, via

its recent investment in AI specialist start-up company Strad-Vision in August

2018. Lastly for LiDAR, Mobis is currently developing a level-3 applicable in-

house model via a partnership with a domestic player and a further level-4

applicable high-end model with a global partner, which Mobis plans to

commercialize by 2025.

Phase-2 (Securing global competitiveness in autonomous driving by 2025):

The key initiative during 2021-25 will be (1) mass production of level-3/ 4

autonomous driving technologies (developed in-house); (2) optimization of radar/

camera systems; and (3) applied technology for autonomous-driving platforms.


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Figure 94. Hyundai Mobis: Autonomous Driving Development Roadmap


Figure 95. Hyundai Mobis: Current ADAS/ Autonomous Driving Product



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Mando: Leading Autonomous Player Expanding Client-Spectrum

Mando is a leading ADAS/autonomous driving technology parts supplier in Korea,

along with Hyundai Mobis. Mando currently shares an ADAS/autonomous driving

parts wallet with Hyundai Mobis supplying Hyundai Motor and Kia Motors. Mando

has successfully commercialized level-“2.5” autonomous driving technology (by

Mando’s definition) such as HDA-II technology. By 2021, Mando aims to initiate

level-3.5 technology (e.g. Highway Driving Pilot), while it targets to launch a full

autonomous driving platform by 2030 (level-5).

Mando has pursed flexibility in technology development with a mixture of in-house

development, partnerships and M&A. The company is currently developing an

autonomous driving platform/Map/AI in collaboration with a Chinese major platform

company, an AI/HD map company targeting, a level-4 & 5 autonomous driving

application, as well as capturing ADAS/autonomous driving business opportunities

in Chinese automakers. For sensor/telecommunication, Mando is also cooperating

with a European semiconductor player and a telecommunication company, while it

is also looking for M&A opportunities to further enhance technologies.

Figure 96. Mando: Active Safety Roadmap & Partnership



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Figure 97. “HOCKEY” – Mando’s Autonomous Vehicle Platform for Testing Technologies


Figure 98. Mando: Global R&D Footprint for ADAS/ Autonomous Driving Development



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Japan Autos National Strategies for Automated Driving and MaaS

The Japanese government has constructed intelligent transport systems using

information and communications technology to promote road safety, transportation

efficiency, and the creation of new transport services.

There are plans for putting rules in place and verifying systems for automated

driving. On the rules front, the government is encouraging private investment as it

looks at formulating forward-looking regulations, aligning vehicle standards with

global standards, and establishing responsibility in the event of personal injury.

Systems testing plans include trialing truck convoys and basing automated driving

services at roadside stations and expressway bus stops.

Since December 2016, when Japan brought its automated driving definitions into

line with that used in the U.S., technological development has followed U.S.

standards. Japan’s original levels three and four have been divided into three, four,

and five. In the new level-3, the system performs all driving tasks within certain

limits and driver responses are required to requests from the system. At level-4, the

system performs all driving tasks within certain limits but no responses to requests

are required. At level-5 there are no limits and no responses are necessary.

Figure 99. Japan : Overview of Automated Driving Level Definitions

Level Automation

Degree Overview

Object and Event Detection and

Response for Safe Driving by:

0 No automation The driver performs all dynamic driving tasks Driver

1 Driver assistance A system performs vehicle driving control sub-tasks in either a longitudinal or lateral direction within an operational design

domain.

Driver

2 Partial automation

A system performs vehicle driving control sub-tasks in both longitudinal and lateral directions within an operational design

domain

Driver

3 Conditional automation

A system performs all dynamic driving tasks within an operational design domain

Where continued activation is difficult, an appropriate fallback response can be made to an intervention request made by the

system.

System

(DDT fallback-

ready driver)

4 High automation A system performs all dynamic driving tasks and can respond within an operational design domain where continued activation is

difficult.

System

5 Full automation A system performs all dynamic driving tasks and can respond within limitation where continued activation is difficult (in other

words, not within an operational design domain)

System

Source: Strategic Headquarters for the Advanced Information and Telecommunications Network Society, Citi Research

Current targets call for reaching level-4 in certain areas by 2020. Driverless mobile

services are planned for certain districts but as of June 2018 these were still only IT

company concepts. The government intends to flesh out systems based on the

development situation and study safety measures. Issues likely to come to the fore

in 2025 in the pursuit of fully-automated driving include the clarification of

responsibility when accidents occur.

Initiatives to promote automated driving


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There are, broadly, three initiatives on the roadmap for commercial use of driverless

systems.

1. Automated driving systems for private vehicles: Fully automated driving on

expressways is targeted for 2025. Public-private research into providing

information on the resulting complex traffic situation began in January 2018.

Safety measures are also a focus, including driver assistance systems and

determining who would need such systems (primarily senior citizens).

2. Automated driving systems for distribution services: Automated driving is

promising for the trucking industry in respects such as labor shortages and

energy saving. The roadmap here is pilot convoys on expressways, platooning

systems in fiscal year 2020, and commercial application in long-distance

haulage in 2022. The hope is that this will lead to full driving automation for

distribution and delivery services.

3. Automated driving systems for mobility services: Mobility for people living

in isolated areas with limited transport has become an issue in the context of

Japan’s shrinking and aging population. The government is targeting

automated driving for public transport in certain areas by 2020 and a national

rollout from 2025.

Local governments are collaborating with IT firms in pursuit of these objectives and

field tests are being conducted across the country (see Figure 100).

Making driverless systems a commercial

reality


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Figure 100. Primary Automated Driving Field Operational Tests Conducted in Japan

Source: Strategic Headquarters for the Advanced Information and Telecommunications Network Society, Citi Research.


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MaaS: Mobility as a Service

Demographics is a key issue in transport policy as Japan’s population ages and

shrinks. The population weightings for working-age people and seniors are currently

64% and 23%, respectively, and the forecast for 2060 is 51% and 40%. The number

of driving license holders over 80 years old is increasing. Road haulage is in a

difficult situation given growing labor shortages. Its high job openings-to-applicants

ratio in comparison with the all-occupation average (2.72 versus 1.35) is the result

of low annual income and long working hours in comparison with other industries as

well as a rising average age. At the same time, rapid growth in inbound demand in

Japan is underscoring the need for new transport services.

Provision of data-based transport services will create greater value-add, and there

are plans to upgrade regional public transport and on-demand logistics services,

use API, etc., for data coordination, and study platform creation. Contributing to

smooth-running transport for the 2020 Olympics is a particular target of public-

private tie-ups and collaboration in information supply and verification testing.

One of the ideas being discussed is integrated MaaS that transcends current

individual transport service modes. By integrating search, reservation, and payment

across a range including trains, buses, and car sharing, for example, public

transport could potentially be made more efficient and more productive. The

application of automated-driving, open-data MaaS in areas such as tourism and

retailing is also being studied.

Automated-driving and MaaS Strategies at Japanese Automakers

Japanese automakers have made big strides forward in MaaS in 2018 as part of

their automated driving strategies. Toyota’s announcement at the 2018 Consumer

Electronics Show (CES) on its e-Palette Concept Vehicle was big news, as was

Honda’s alliance with GM. There have been few announcements on the technology

front, however, as automakers pushed on with autonomous vehicle development.

We think there may be a flurry of action in the run-up to the October 2019 Tokyo

Motor Show and 2020 Tokyo Olympics. Most notably, the Japan Automobile

Manufacturers Association is holding a public automated-driving “verification testing”

event for Japanese OEMs in July 2020, just before the Olympics begin. Ten

companies will demonstrate 80 level-2 to level- 4 (SAE standard) vehicles on roads

around Haneda airport, between Haneda airport and Tokyo Waterfront City and

central Tokyo, and in the Tokyo Waterfront City area. This should provide a useful

update on each maker’s automated-driving and MaaS strategies.

Toyota supplier collaboration is a highlight on the supplier side. In August 2018

agreements were announced for joint ventures in the fields of automated driving

(integrated ECUs for automated driving and vehicle control) and electrification (drive

modules for a broad range of EVs).

Toyota: Big strides in MaaS

Toyota unveiled its e-Palette Concept Vehicle — an electric, connected,

autonomous, MaaS specialty vehicle — at the January 2018 CES. The low floor and

box shape provide a spacious interior that can be fitted according to the specs of

Toyota’s service partners, whose businesses include ride-sharing, hotels, and retail

stores. Disclosure of the control interface to firms working on the development of

automated-driving kits is a particularly notable feature. Vehicle control technology is

a Toyota forte and we assume that Toyota’s aim in opening it up to third parties is to

promote the use of its Mobility Services Platform.

Japan’s shrinking and aging population

requires new transport services

Toyota and Honda are the focus in 2018

with all firms shifting into gear for the 2020

Olympics

Toyota business strategy taking shape in

autonomous vehicles and MaaS


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The launch partners Toyota announced included illustrious mobility players Amazon,

Didi Chuxing, Pizza Hut, Mazda, and Uber. Toyota plans to deploy a vehicle with

some automated functionality at the Tokyo Olympics and Paralympics in 2020 and

run trial services in various parts of the U.S. in the early 2020s.

Mazda’s inclusion was seen as a surprise but Toyota has chosen Mazda as a

partner for electric vehicle technologies such as driving range extension. Mazda has

a prototype that uses a rotary-engine range extender and now it looks as though

this might be used for Toyota Mobility Services. Mazda is planning a battery EV

launch in 2019 but also preparing a model with a range extender. Rotary engines

are well suited to EVs because they are quiet and low-vibration.

Major news on strategy for actual use of e-Palette came through in October 2018,

when Toyota announced agreement on a strategic alliance for the creation of new

mobility services. The first step is the establishment of a joint venture, Monet

Technologies. Mobility supply/demand is to be optimized by linking Toyota’s Mobility

Services Platform, with its accumulated vehicle data, with its partners’ IoT platform,

which collects people flow data. The starting part will be manned ride-hailing

services. e-Pallette operations are to commence in the mid-2020s, and further down

the road the companies are looking at overseas rollouts as a Japanese alliance. It

was Toyota that made the proposal. We presume Toyota felt an urgent need to be

allied with a firm that is overwhelming other companies in MaaS investment globally

to enable success for its MaaS business.

Toyota has acknowledged that one reason for linking up in a joint venture was that

its partner already had stakes in many of its selected targets. Plans include the

deployment of Toyota Sienna minivans fitted with the Toyota Guardian automated

vehicle control system and Uber’s autonomous driving kit on Uber ride-sharing

networks from 2021. The two companies will also consider the operation of mass-

produced automated-vehicles, including third-party operators. We think this

comprehensive tie-up with a firm that has high share in car-sharing markets should

lead to widespread adoption of Toyota’s automated-driving system, enabling it to

gather big data through vehicle data communication modules, expand its connected

car business earnings, and raise operating rates in vehicle production. Uber’s global

network and Toyota’s reputation for safety and quality appear to be a good match.

Figure 101. Conceptual Map of Toyota’s Mobility Services Platform

Source: Company Data, Citi Research


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Honda: Honda Collaborating with GM on Driverless Ride-sharing

Honda announced collaboration with GM on the development of autonomous

vehicles for ride-sharing in October 2018, adding to its cutting-edge agreements

with GM for fuel-cell vehicles and batteries. In addition to a driverless R&D

agreement with Waymo, Honda is investing $750 million in GM-Cruise Holdings (a

roughly 5% stake) and also plans to provide business capital of $2 billion over the

next 12 years. In addition to joint development of multi-purpose vehicles for

driverless ride sharing, Honda, GM, and Cruise will aim to roll out driverless ride-

sharing services globally. The point we see for Honda is whether it can create a

standout presence in the three-company alliance by leveraging its strengths in

packaging and interior/exterior design and its attractive points of contact with

customers, which include motorcycles.

Yamaha Motor: Driverless Niche Strategy

Yamaha Motor is in a unique position in driverless vehicles. Active in niche sectors,

it has a 90% share of Japan’s market for unmanned helicopters for spraying

agricultural chemicals. Under an agreement with NVIDIA announced in September

2018, Yamaha is to use the NVIDIA GPU computing system NVIDIA® Jetson™

AGX Xavier™ in a wide range of products including unmanned ground vehicles

(agricultural UGVs), last-mile vehicles based on golf carts, industrial-use drones,

industrial-use unmanned helicopters, and unmanned boats. Driverless vehicles

were a focus in the 2030 Vision Yamaha announced in December 2018. Examples

of the company’s success in niche strategy include the top global share in outboard

motors and watercraft and the top Japan share in FRP pools. We believe Yamaha

puts its intellectual capital to effective use, deploying motorcycle and car engine

technology in outboard motors, for example, and using FRP technology for boats. It

is looking to carve out a driverless vehicle niche by combining proprietary

technologies such as image recognition technology developed in robotics with

technologies acquired from other firms through venture capital investments and

alliances.

Denso: Quietly Accumulating Elemental Technology

Denso is accelerating development of its driverless vehicle framework. In April

2018 it (1) opened its Global R&D Tokyo office in Minato Ward for autonomous

driving R&D and (2) commenced R&D in Israel on cutting-edge technologies in

areas including autonomous driving, cyber security, and AI. Denso is combining

proprietary development with tie-ups with local firms and universities. In October

2018 the company announced the establishment of a development and testing

facility for automated-driving technologies at Haneda airport. This is due to open in

June 2020. It will have a test course and function as a center for mobility systems

development.

In cutting-edge areas, Denso is forging external ties as it accelerates accumulation

and development of elemental technologies. Key steps in 2018 included (1)

investing in ActiveScaler (a U.S. developer of managed MaaS systems powered by

AI), (2) investing in Dellfer (U.S. developer of cybersecurity technology), (3)

increasing its stake in Renesas Electronics (Japan) to 5% from 0.5%, (4) investing

in On The Road (Japanese developer of large-scale systems using communications

and cloud-computing technology), (5) investing in Metawave (U.S. holder of core

technologies for extending radar’s detection range, boosting its recognition

functionality, and creating smaller products), (6) increasing its stake in ThinCI (U.S.)

with the aim of speeding up Data Flow Processor development, (7) forming a joint

venture with NRI Secure Technologies (a cybersecurity business focused on in-

Strengthening alliance with GM; key is how

much of a presence it can establish

Yamaha Motor pursuing niche strategy in

driverless vehicles

Strengthening its proprietary framework,

forging alliances to accelerate accumulation

and development of elemental technologies


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vehicle electronic products diagnosis), and (8) investing in Infineon (German

possessor of cutting-edge technology in automotive semiconductors).

Toyota Suppliers Mobilizing Group Strength Via Joint Ventures

Collaboration between Toyota suppliers on cutting-edge technology has been a

notable development in 2018. Two agreements were announced in August. Denso,

Aisin Seiki, Advics, and JTEKT agreed to study the formation of a joint venture for

developing integrated ECU software for automated driving and vehicle dynamics

control. They are aiming to set up a company in March 2019 with stakes of 65% for

Denso, 25% for Aisin, and 5% each for Advics and JTEKT. They will seek greater

automated driving sophistication by combining their respective sensor, steering, and

brake hardware with integrated ECUs. Under a separate agreement, Aisin Seiki and

Denso are looking to form a 50:50 joint venture in March 2019 for development and

sale of drive modules (transaxle/motor generator/inverter packages) for xEVs. They

will aim for a product lineup covering hybrid EVs (HEVs), plug-in hybrid EVs

(PHEVs), fuel-cell EVs (FCEVs), and EVs.

Significant step toward avoiding in-group

competition and resource duplication

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Figure 102. Business Strategies at Japanese Automakers

Clean energy autos Automated driving Other (connected cars, etc.)

Nissan, MMC

EV PHV

FCV

Developing eight new EVs and are actively introducing new models in China

Aim to increase annual EV sales to 1mn units by FY2022, and to reduce charging time to 15 minutes and increase driving range per charge to 230km by 2022. PHVs will use MMC

technology Takes stake in Enevate as lithium-ion battery technologies

evolve

Began joint development with Daimler and Ford in 2013. The planned release of a FCV using a jointly-developed system by

around 2017 has been delayed. The partners have announced new technology that uses

bioethanol.

ADAS

Automated driving

Plans to install Propilot, an autonomous driving safety system, in 20 models by 2022.

Plans to develop a fully-autonomous vehicle (no driver required) by 2022, and is conducting research on

remote control with NASA. Nissan has announced it will supply autonomous driving vehicles to DeNA.

Makes strategic investment in WeRide.ai

Telematics

Nissan has joined Alliance Connected Cloud

The alliance provides a foundation for expanding mobility services, including unmanned vehicle dispatch services

Field testing for Easy Ride, a self-driving taxi service developed with DeNA, started in

March 2018

Alliance with Google in next-generation infotainment systems

Toyota

HV PHV

EV

FCV

Target

Toyota plans to expand its global EV lineup to more than 10 models by the first half of the 2020s; it is collaborating with Mazda and others in the EV space and has a battery tie-up with Panasonic. Toyota plans to introduce an EV with an all

solid-state battery in the first half of the 2020s (small production volume).

Toyota plans to assemble passenger and commercial FCV lineups in the 2020s.

Aims to increase global sales to 5.5mn units in 2030, including zero emission vehicle (EV and FCV) sales of 1mn units.

ADAS

Automated

driving

Toyota started introducing second generation "Toyota safety sense" technology in 2018 and is installing it in a

broad range of models, including compacts.

The Toyota Research Institute Advanced Development (TRI-AD), a joint venture with Denso and Aisin Seiki, has been established to oversee the development of

autonomous driving technology.

Toyota plans to commercialize autonomous driving technology for use on highways by 2020 and for use on general roads in the first half of the 2020s. Plans call for TRI technology to be commercialized by around 2025. With a joint venture partners, establishes MONET, a

new firm specializing in MaaS

Telematics

Toyota is developing new growth strategies based on mobility service platforms

Toyota announced a mobility-as-a-service (MaaS) concept EV at CES 2018. Partners

include Amazon.

Toyota is moving toward introducing in-vehicle connectivity as a standard feature and

taking measures to secure big data

Toyota is forming alliances with taxi companies in Japan

Mazda

EV PHV

ICE

Target

Mazda plans to release an EV in 2019 and a PHV sometime from 2021. Mazda is collaborating with Toyota in the EV

space.

SkyactivX will be rolled out from FY3/19. Clean diesel development is ongoing.

Mazda aims to reduce its companywide average CO2 emission level by 50% compared with 2010 by 2030 (Well to

Wheel)

ADAS

Automated

driving

Mazda plans to make i-ACTIVSENSE advanced safety technologies a standard feature of its vehicles. These

technologies include automated braking to avoid/reduce the severity of collisions, acceleration control for AT,

blind spot monitoring, and rear cross traffic alerts.

Mazda is progressing with the development of autonomous driving technologies based on the Mazda

Co-Pilot Concept and plans to start verification testing in 2020 and introduce technologies as standard features

by 2025.

Telematics Mazda has developed a proprietary car

connectivity system called Mazda Connect and plans to collaborate with Toyota.

Honda

EV PHV

FCV

Target

Honda plans to release an EV based on the "urban EV concept" in Europe in 2019

Honda has a battery tie-up with GM and a motor tie-up with Hitachi

Honda is conducting joint development with GM and plans to introduce a new model around 2020

Honda forecasts xEVs will account for two thirds of auto sales by 2030.

ADAS

Automated

driving

Honda is stepping-up the introduction of Honda sensing technology

Honda aims to develop Level 4 autonomous driving technology by around 2025

Conducting a five-year R&D project on autonomous driving AI technology with SenseTime (China)

Negotiating a partnership with Waymo

Collaborating with GM's Cruise unit in the development of vehicles for driverless ride-sharing services

Telematics

Honda introduced Internavi, the world's first advanced traffic information service, in 2003.

Internavi uses probe data collected from vehicles. The launch of a free telematics service in 2010 drastically increased the volume of collected data. In addition to providing precision navigation support,

Internavi also has a significant social role; for example, by providing map information of

actual traffic conditions in the event of disaster. Honda and Toyota are trialing an

accident notification system called D-Call Net

Honda is collaborating with ride-share clubs


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Figure 103. Business Strategies at Japanese Automakers

Clean energy autos Automated driving Other (connected cars, etc.)

Suzuki

EV PHV

FCV

Suzuki aims to develop an EV motor by 2020.

Suzuki plans to release an EV in India with Toyota in 2020.

Suzuki is leading the development of FCV motorcycles in a tie-up with Intelligent Energy (UK)

ADAS

Automated

driving

Suzuki plans to develop brake support systems using lasers/cameras as well as automated parking and other everyday driving assist technology. Automated braking

support will use several systems.

Suzuki is the process of accumulating advanced autonomous driving technologies and does not have a timeframe for commercialization. The collaboration with

Toyota looks promising.

Telematics Collaboration with Toyota is promising

SUBARU EV

PHV

Subaru plans to release a PHV using Toyota technology in 2018 to respond to North American ZEV regulations.

Subaru plans to release a PHEV in 2018 and an EV in 2021

ADAS

Automated

driving

Subaru is developing and advanced driver assist system called EyeSight

Subaru created a highway same-lane congestion tracking function in 2017

Subaru aims to commercialize autonomous driving technologies, including highway lane-change assist, in

2020

Telematics Subaru has adopted a proprietary system in

North America. The collaboration with Toyota looks promising longer term.

Yamaha Motor

EV PHV 2W

Looking into collaborating with Gogoro in the shared use of battery replacement systems and the outsourced development

and production of electric scooters/motorcycles

Automated

driving

Collaboration with Nvidia to push the automation of driverless agricultural vehicles and last-mile vehicles by

making them smarter

Has started a local transport business for construction-use materials and equipment using pilot-less industrial-

use helicopters

Trialing low-speed autonomous driving vehicles in the city of Iwata

Telematics

Has established the Yamaha Motor Advanced Technology Center as a base for

the development of advanced tech in robotics, AI, and IT

Denso

GE DE EV

PHV

EV C.A. Spirit, a joint venture with Toyota and Mazda, established to develop basic concept EV technologies

Due to set up a JV in development and sales of drive modules with Aisin Seiki in March 2019

ADAS

Autonomous

driving

Denso established an ADAS/autonomous driving R&D base in Tokyo in 2018

Denso started R&D on autonomous driving, cyber security, AI, and other advanced technologies in Israel

in April 2018.

Due to set up a JV in the development of integrated ECUs with other Toyota affiliates in March 2019

Telematics Driving safety telematics service G500Lite

Aisin HV

PHV

Aisin started volume production of a new transmission for HVs and PHEVs in 2018

Due to set up a JV in development and sales of drive modules with Denso in March 2019

Aisin plans to commercialize an EV powertrain system by 2020

Autonomous

driving

Aisin has invested in the Toyota Research Institute Advanced Development.

In addition to conducting joint technology development, the three companies with invest more than ¥300bn in

development activities.

Due to set up a JV in the development of integrated ECUs with other Toyota affiliates in March 2019

Telematics

Aisin is developing technologies that use telematics to protect pedestrians at

intersections (using data gathered by information centers from pedestrians

carrying mobile phones), provide alternative routes in the event of accidents (we believe

this includes vehicle-to-vehicle and roadside-to-vehicle communications), and

other services.



© 2018 Citigroup

121

Connectors/Sensors: A Major Beneficiary of Vehicle Electrification As the installation of electronic systems advances, the number of electronic circuits

that exchange information and hence the number of connectors will increase. Given

the increase in electronic circuits will be exponential to the number of system

installations we forecast annual average volume growth of 4%-6% versus just over

2% for ECUs. We believe auto volume growth and an increase in the number of

connectors per vehicle will result in the automotive connector market expanding

from $14.3 billion in 2017 to $16.1 billion in 2018, and $22.3 billion in 2023 (Figure

104 and Figure 105).

While current auto cycle demand appears unfavorable to auto production with a

deceleration in global SAAR from prior years, we note major connector companies

have indicated connector content per vehicle is unaffected and is drifting toward the

high end of the content growth range as auto OEMs continue to increase electronic

content in cars plus EV penetration which helps connector content per vehicle. In

addition to the car electrification trend, we highlight two incremental drivers below

for content growth in automotive connector industry.

Connector companies continue to make acquisitions in the sensor industry

(a downstream for connector companies): Auto-use sensors are the eyes of

electronic systems, monitoring information inside and outside the vehicle. There

are more than 20 types of sensors, including oxygen and emission sensors and

knock sensors for engines, current sensors for xEVs, angular velocity sensors for

ESC, and radar sensors and ultrasonic sensors for ADAS. Fuel economy and

emission regulations have already led to engine oxygen and nitrogen oxide

sensors becoming commonplace. One noticeable trend in automotive sensor

industry is connector companies continue to make acquisitions in the sensor

industry for vertical integration. (i.e. Amphenol acquired GE Advanced Sensor

business and Casco and TE Connectivity acquired Measurement Specialty). We

believe connector companies could benefit from automotive sensor acquisitions

as connector companies leverage existing relationship with auto OEMs to

expand sensor/connector integrated product offerings. We expect M&A activities

within automotive sensor industry are likely to continue and believe big

connector/sensor companies can create synergies from industry consolidation by

leveraging their global manufacturing footprint, design capabilities and sales

channels with major Auto OEMs.

Technology transition from diesel to electric vehicles (EV) likely to drive

incremental connector content growth: After the Volkswagen diesel defect

device issue in late 2015, European auto OEMs have been accelerating the

technology development in EV to replace diesel product offerings. We view the

current technology transition from diesel to EV as a positive to connector

companies as the connector content dollar amount in EV is 50% more compared

to connector content in diesel vehicles (diesel and combustion vehicles have

similar dollar content), primarily due to voltage and power management required

in EVs. On the other hand, less diesel penetration is a headwind for sensor

companies, particularly non-optical sensors, as sensor content in diesel vehicles

is ~50% higher than EV and combustion vehicles.

We forecast annual average connector

volume growth of 4%-6% per vehicle in

addition to annual auto production growth of

2-3% less average price declines of 0-2%

resulting in organic connector growth of 6-8%.

TE Connectivity the largest automotive

connector company indicated the auto

connector content growth is at the high end

of 4-6%


© 2018 Citigroup

122

Major auto-use connector makers include Yazaki, Sumitomo Wiring Systems, Japan

Aviation Electronics, Hirose Electric, Iriso Electronics, JST (Japan), TE Connectivity,

Delphi, Molex, and Amphenol. The number of suppliers is large because the type of

connector used differs by application. Even so, we estimate TE Connectivity has a

market share of 30%-40% and is the dominant player.

Figure 104. Connector Content Per Vehicle (2003-2017)

Figure 105. Connector Content YoY Growth

Source: Citi Research, Bishop Source: Citi Research, Bishop Note: We believe automotive connector was flat to up

lows single-digit in 2015 on constant currency basis vs. -6.4% in US$ due to EUR depreciation

Figure 106. We Forecast Connectors Will Be A Beneficiary of Vehicle Electrification


$50

$70

$90

$110

$130

$150

$170

$190

$210

2003 2005 2007 2009 2011 2013 2015 2017

Unweighted Weighted Weighted (no negative content growth)

-30%

-20%

-10%

0%

10%

20%

30%

200

3

200

4

200

5

200

6

200

7

200

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200

9

201

0

201

1

201

2

201

3

201

4

201

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201

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201

7

Unweighted Weighted

avg weighted growth = 6.5%

avg unweighted growth =

7.0% FX headwind in 2015

0

5,000

10,000

15,000

20,000

25,000

2009 2011 2013 2015 2017 2019E 2021E 2023E

($mn)

Major auto-use connector makers


© 2018 Citigroup

123

Figure 107. Top 30 Connector Manufacturers 2012-2017

Source: Bishop and Citi Research.

Note: We highlight the FX headwind to connector industry in 2015 which caused industry decline -6.1% in USD (or down -0.5% on constant currency basis). We note automotive connector industry was down -6.4% in 2015 and believe the constant currency growth rate is at flat to up low single digit given that higher connector content value in European autos.

Figure 108. Top 10 Connector Manufacturers Segment Rankings (2017)

Source: Bishop and Citi Research. Note 2013 data not yet available.

Top 30 Connector Manufacturers

$ Millions 2012 2013 2014 2015 2016 2017 2012 2013 2014 2015 2016 2017

Y/Y Y/Y Y/Y Y/Y Y/Y Y/Y Market Market Market Market Market Market

Rank Manufacturer 2012 2013 2014 2015 2016 2017 Change Change Change Change Change Change Share Share Share Share Share Share

1 TE Connectivity $8,482 $8,719 $8,943 $8,211 $8,573 $9,396 0% 3% 3% -8% 4% 10% 17.8% 17.0% 16.1% 15.8% 15.8% 15.6%

2 Amphenol Corporation $4,015 $4,290 $4,993 $5,238 $5,922 $6,607 9% 7% 16% 5% 13% 12% 8.4% 8.4% 9.0% 10.1% 10.9% 11.0%

3 Molex Incorporated $3,580 $3,617 $3,911 $4,169 $4,315 $5,222 0% 1% 8% 7% 3% 21% 7.5% 7.1% 7.1% 8.0% 8.0% 8.7%

4 Aptiv (Delphi Connection) $2,589 $2,953 $2,701 $2,736 $2,931 $3,076 3% 14% -9% 1% 7% 5% 5.4% 5.8% 4.9% 5.3% 5.4% 5.1%

5 Foxconn (Hon Hai) $2,683 $2,704 $2,482 $2,328 $2,518 $2,927 -1% 1% -8% -6% 8% 16% 5.6% 5.3% 4.5% 4.5% 4.6% 4.9%

6 Yazaki $2,278 $2,382 $2,409 $2,459 $2,570 $2,588 5% 5% 1% 2% 5% 1% 4.8% 4.7% 4.3% 4.7% 4.7% 4.3%

7 JAE $1,311 $1,311 $1,503 $1,428 $1,528 $2,056 21% 0% 15% -5% 7% 35% 2.8% 2.6% 2.7% 2.7% 2.8% 3.4%

8 LuxShare N/A $595 $942 $1,139 $1,483 $1,778 58% 21% 30% 20% 1.2% 1.7% 2.2% 2.7% 3.0%

9 JST $1,357 $1,445 $1,394 $1,321 $1,435 $1,534 -10% 6% -4% -5% 9% 7% 2.9% 2.8% 2.5% 2.5% 2.6% 2.6%

10 Rosenberger $625 $720 $900 $920 $1,035 $1,253 -1% 15% 25% 2% 13% 21% 1.3% 1.4% 1.6% 1.8% 1.9% 2.1%

11 Hirose $948 $1,087 $1,065 $1,017 $1,046 $1,139 -18% 15% -2% -5% 3% 9% 2.0% 2.1% 1.9% 2.0% 1.9% 1.9%

12 Sumitomo Wiring Systems $1,006 $976 $992 $902 $981 $1,042 17% -3% 2% -9% 9% 6% 2.1% 1.9% 1.8% 1.7% 1.8% 1.7%

13 JONHON Optronic (China Aviation Optical-Electrical ) N/A $427 $467 $639 $749 $800 10% 37% 17% 7% 0.8% 0.8% 1.2% 1.4% 1.3%

14 HARTING $616 $662 $726 $629 $648 $764 -8% 7% 10% -13% 3% 18% 1.3% 1.3% 1.3% 1.2% 1.2% 1.3%

15 Samtec $515 $565 $613 $625 $661 $713 5% 10% 8% 2% 6% 8% 1.1% 1.1% 1.1% 1.2% 1.2% 1.2%

16 Fujikura $255 $572 $650 124% 14% 0.5% 1.1% 1.1%

17 3M Electronic Solutions Division $576 $610 $920 $567 $509 $564 -5% 6% 51% -38% -10% 11% 1.2% 1.2% 1.7% 1.1% 0.9% 0.9%

18 Shenzhen Deren Eletr. Co $352 $316 $449 $563 N/A -10% 42% 26% 0.6% 0.6% 0.8% 0.9%

19 Phoenix Contact $399 $436 $470 $467 $479 $546 89% 9% 8% 0% 3% 14% 0.8% 0.9% 0.8% 0.9% 0.9% 0.9%

20 Korea Electric Terminal Co $320 $424 $470 $457 $434 $476 6% 33% 11% -3% -5% 10% 0.7% 0.8% 0.8% 0.9% 0.8% 0.8%

21 CommScope N/A $432 $468 $457 $492 $456 9% -2% 8% -7% 0.8% 0.8% 0.9% 0.9% 0.8%

22 AVX/Elco $481 $501 $449 $356 $422 $446 4% 4% -10% -21% 18% 6% 1.0% 1.0% 0.8% 0.7% 0.8% 0.7%

23 Carlisle $336 $373 $417 $419 N/A 11% 12% 0% 0.6% 0.7% 0.8% 0.7%

24 Belden $463 $468 $428 $401 $403 $408 54% 1% -9% -6% 0% 1% 1.0% 0.9% 0.8% 0.8% 0.7% 0.7%

25 Radiall $283 $312 $365 $333 $360 $385 0% 10% 17% -9% 8% 7% 0.6% 0.6% 0.7% 0.6% 0.7% 0.6%

26 IRISO Electronics $311 $336 $352 $316 $345 $377 4% 8% 5% -10% 9% 9% 0.7% 0.7% 0.6% 0.6% 0.6% 0.6%

27 Bel Connectivity $310 $339 $296 $345 N/A 9% -13% 16% 0.6% 0.7% 0.5% 0.6%

28 Glenair $300 $303 $315 $331 N/A 1% 4% 5% 0.5% 0.6% 0.6% 0.6%

29 Huber+Suhnei $298 $311 $336 $301 $314 $330 -9% 4% 8% -10% 4% 5% 0.6% 0.6% 0.6% 0.6% 0.6% 0.5%

30 Lotes $240 $256 $320 6% 25% 0.5% 0.5% 0.5%

31 ITT Interconnect Solutions $377 $397 $399 $328 $309 $318 -9% 5% 0% -18% -6% 3% 0.8% 0.8% 0.7% 0.6% 0.6% 0.5%

32 Souriau $376 $364 $324 $299 $294 $306 6% -3% -11% -8% -1% 4% 0.8% 0.7% 0.6% 0.6% 0.5% 0.5%

Total Top 30 $35,984 $38,891 $42,022 $42,251 $45,631 $47,509 2% 8% 8% 1% 8% 4% 76% 76% 76% 81% 84% 79%

All Others $11,626 $12,292 $13,380 $9,799 $8,532 $12,607 -15% 6% 9% -27% -13% 48% 24% 24% 24% 19% 16% 21%

Total Market $47,610 $51,183 $55,402 $52,050 $54,163 $60,116 -3% 8% 8% -6% 4% 11% 100% 100% 100% 100% 100% 100%

Top 10 Connector Manufacturers - Segment RankingsComputers Business Telecom

World and Consumer Retail Datacom Industrial Transportation Automotive Medical Military

Rank Peripherals Electronics Education Equipment Instruments Equipment Equipment Equipment Equipment Aerospace Other

1 Foxconn Molex Molex Amphenol LuxShare Amphenol Aptiv TE Connectivity Molex Amphenol TE Connectivity

2 Molex TE Connectivity TE Connectivity Molex Molex TE Connectivity TE Connectivity Yazaki TE Connectivity JONHON Aptiv

3 LuxShare J.S.T. J.S.T. JAE TE Connectivity Molex Amphenol Aptiv Amphenol Glenair Hirose

4 Amphenol LuxShare Foxconn TE Connectivity Rosenberger HARTING Molex JAE LEMO SA Carlisle Sumitomo

5 Shenzhen Deren Commscope IRISRO LuxShare Foxconn J.S.T. Carlisle J.S.T. Fujikura/DDK Bel ITT

6 LOTES Co. Ltd IRISO Fujikura/DDK Rosenberger LEMO SA Phoenix Contact Yazaki Rosenberger Luxshare Radial JAE

7 Foxlink Hirose 3M CommScope Samtec Belden Sumitomo Sumitomo 3M TE Connectivity Amphenol

8 JAE Amphenol Hirose Hirose Hosiden Weidmuller Korea Electric AVX Samtec Aptiv Foxconn

9 I-PEX JAE Sumitomo Foxconn Radiall Fujikura/DDK Lear Amphenol ODU Souriau Molex

10 Samtec Aptiv Shenzhen Deren JONHON IRISO Samtec Souriau Molex Radiall AMETEK Korea Electric


© 2018 Citigroup

124

Figure 109. Top 30 Connector Manufacturers – Regional Ranking (2017)

Top 30 Connector Manufacturers - Regional Sales Rank

World North Asia

Rank Manufacturer America Europe Japan China Pacific ROW

1 TE Connectivity 1 1 1 5 1 2

2 Amphenol 2 2 8 2 2 1

3 Molex 3 4 3 4 4 4

4 Aptiv (Delphi Connection) 4 3 45 8 12 6

5 Foxconn (Hon Hai) 15 21 23 1 5 8

6 Yazaki 5 7 2 11 3 3

7 JAE 21 37 5 6 6 11

8 LuxShare 67 64 22 3 20 19

9 JST 8 23 4 13 8 74

10 Rosenberger 10 6 27 17 7 10

11 Hirose 29 39 6 10 11 54

12 Sumitomo Wiring Systems 33 42 7 14 10 5

13 JONHON (China Aviation Optical Elect) 80 67 85 7 78 75

14 HARTING 27 5 24 23 41 22

15 Samtec 7 9 34 18 17 34

16 Fujikura/DDK 74 31 12 12 27 9

17 3M Electronic Solutions Division 19 27 11 20 19 14

18 Shenzhen Deren 84 88 86 9 15 31

19 Phoenix Contact 22 8 63 21 24 30

20 Korea Electric Terminal Co 85 89 28 19 9 7

21 CommScope 7 36 36 22 21 36

22 AVX/Elco 27 12 9 24 60 90

23 Carlisle 6 28 53 67 43 25

24 Belden 13 19 26 35 36 10

25 Radiall 15 15 33 42 44 39

26 IRISO Electronics 39 39 13 32 17 13

27 Bel Connectivity 16 34 54 61 16 58

28 Glenair 11 25 57 95 63 19

29 HUBER+SUHNER 35 11 40 44 20 16

30 LOTES 81 80 46 15 14 17



© 2018 Citigroup

125

Figure 110. Sensor Control Per Vehicle

Figure 111. Sensor Content YoY Growth

Source: Company Reports, Citi Research Source: Company Reports, Citi Research

Figure 112. Outline of Major Automotive Sensors

Sensor Application Outline

Oxygen sensor Engine Monitors oxygen concentration in the engine. Penetration almost complete.

A/F sensor Engine Monitors the engine air-fuel ratio. Penetration almost complete.

NOx sensor Engine Monitors NOx concentration in the exhaust. Penetration almost complete.

Knock sensor Engine Monitors knocking caused by an increase in engine pressure. Penetration almost complete.

Air flow meter/Vacuum sensor Engine Measures the quantity of air going into the engine

Pressure sensor Engine Monitors engine intake pressure, turbo pressure, common rail pressure

Magnetic sensor Engine/Body Monitors vehicle angle and position

Temperature sensor Engine/xEVｓ Monitors temperature changes in the engine. Used for batteries and motors.

Current sensor X EVs/Lead batteries Measures the electric current used by electrified vehicles.

Air pressure sensor TPMS Monitors tire pressure

Torque sensor EPS Monitors power steering torque.

Rudder angle sensor ESC Monitors vehicle steering direction

Yaw rate sensor ESC Monitors the rate of vehicle rotational angle change

Gyro sensor ESC/Car navigation Monitors the change in vehicle angular velocity; used by ESC and car navigation (positional information)

Acceleration sensor ESC/Air bag Monitors vehicle acceleration; used by ESC and airbag collision detection systems

Ultrasound sensor ADAS Used by parking assistant and internal detection systems

Auto camera sensor ADAS Used by preventive safety technologies (automatic braking, LDW, ACC, automated parking, etc.,)

Radar sensor ADAS Used by obstacle detection systems (automatic braking, ACC, etc.,)

Source: Company Data, Denso, Citi Research

$0

$50

$100

$150

$200

$250

$300

$350

2003 2005 2007 2009 2011 2013 2015 2017

Unweighted Weighted Weighted (no negative growth) -10%

-5%

0%

5%

10%

15%

20%

25%

30%

35%

2009 2010 2011 2012 2013 2014 2015 2016 2017

Unweighted

avg weighted growth = 7.1%avg unweighted growth = 4.6%


© 2018 Citigroup

126

Japanese Electronics Sector on Autonomous Driving/ADAS

Semi’s in Japan: Renesas Electronics & Toshiba

In Japan, Renesas Electronics and Toshiba are key semiconductor makers involved

in autonomous driving/ADAS logic chips. Renesas’ mainstay microcontrollers

(MCUs) are used primarily in electronic vehicle control and are an essential product

for vehicle electrification. In high-end autos their installation count is rising mainly in

tandem with increasing adoption of safety systems (ADAS). In mid-range and high-

end models growth in power components is driving growth in installation.

Figure 113. SoC-MCU Relationship, An Example of an Automotive Control System


We think the automotive MCU market can grow at an annual pace in the mid-single-

digits by value over the next few years driven by increasing installation volume.

Renesas has maintained the top share of the market (approximately one-third). The

major competitor in the safety/ADAS space is Infineon Technologies, which has

strong relationships with European Tier-1 automakers. There is also significant

competition in automotive MCUs from NXP, Texas Instruments, and Microchip

among others.

SoC

Camera Milliwave radar Sensors

Information

MCU MCU MCU

Processing

Command

Steering Engine Actuators


© 2018 Citigroup

127

Figure 114. Automotive MCU Market Share (2017)

Source: Strategy Analytics, Citi Research

Renesas is also the developer/marketer of the R-Car series automotive system-on-

chip (SoC), which is targeted at adoption in auto OEMs’ autonomous driving

systems. Two firms have a strong presence in this market: NVIDIA — which

leverages graphics processing unit (GPU) features — and Intel, which bought the

pioneer in single-lens camera automatic braking systems, Mobileye. NVIDIA has

alliances/joint development arrangements with VW/Audi, Daimler, Tesla, and

Toyota, while Intel-Mobileye are working with BMW.

Figure 115. Renesas: Solution Kit Equipped with R-Car H3

Figure 116. NVIDIA: Autonomous Driving Development Board

Source: Renesas Electronics, Citi Research Source: NVIDIA, Citi Research

Others16%

Renesas Electronics

28%

Microchip15%

Infineon Technologies

8%

Texas Instruments

8%

NXP Semiconductor

25%


© 2018 Citigroup

128

Renesas has not made collaboration announcements but from factors such as its

microcontroller (MCU) supplies we infer that it is involved in several companies’

R&D. Toyota will be using Renesas’s R-Car H3 and MCUs in the (expressway)

autonomous driving system it plans to introduce in 2020 (Toyota has also

announced joint development with NVIDIA). High-power chips are required for high-

speed, high-volume data processing in autonomous driving AI, but are also

important for mass market models safety and low power consumption (low heat

generation). We assume Renesas is highly competitive from the chip safety and

power consumption standpoint and we see the potential for announcements of

adoption by other automakers besides Toyota. We think Renesas could fill the

vacant third spot behid NVIDIA and Mobileye in SoC.

Toshiba manufactures/sells image recognition chips under the Visconti brand name.

They are used mainly for image recognition in front view monitoring cameras.

Toshiba has a business tie-up with Denso for Visconti and it is supplying image

recognition processors for the Toyota Safety Sense ADAS system via Denso. In

addition to Toshiba/Visconti, major players in processors for cameras monitoring

vehicle surroundings include SoC players Mobileye (Eye-Q), Texas Instruments,

NXP, Renesas (e.g. R-Car V2H), and FPGA player Xilinx.

Figure 117. Renesas MCU and Toshiba Visconti Included in Toyota Prius Safety Sense-P Front Camera Module

Source: Fornalhaut Techno Solutions, Citi Research

Application Processor (Toshiba)

(Image Recognition Processor for ADAS)

TMPV7506XBG

Microcontroller (Renesas)

R5F74593LBG


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Figure 118. Supplier Matrix for Major Automotive Sensors

Denso Hitachi MELCO Panasonic Nidec Elesys

TDK Murata Omron Nicera Infineon Bosch Sensata

Technology Aptiv

Oxygen sensor X X X X

Air flow meter X X X X

Pressure sensor X X X X X X X X

Temperature sensor X X X X X X X X

Current sensor X X X X X X

Ultrasound sensor X X X X X

Auto camera sensor X X X X X X X X

Radar sensor X X X X X X X X X

Rudder angle sensor X X X X X

Yaw rate sensor X X X X

Gyro sensor X X X X

Acceleration sensor X X X X X X X

Air pressure sensor X X X

Automotive antenna X X X X X X

GPS unit X X X X X

Source: Company Data, Nikkei Automotive Technology, IRC, Citi Research Note: This chart does not include all products by suppliers or suppliers for specific products.


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Autonomous Trucks Thus far, our discussion has mostly centered on urban mobility and light vehicle

transportation. We believe autonomous trucks are at the top of the pyramid for

disruption in trucking, as they address the single biggest issue that the industry

faces — the cost of employing drivers. Getting drivers out of trucks would be

revolutionary from a cost perspective, as drivers are typically trucking companies’

largest operating expense, and we estimate a commercial level-5 autonomous truck

would produce nearly 50% savings per mile (versus current long-haul tractors). That

said, autonomous trucks have high regulatory and legislative hurdles, in addition to

potential infrastructure hurdles, and it could be many years before a full regulatory

update allowing autonomous trucks to engage in interstate operations takes place.

Ultimately, while some automation technology for trucks appears quite close to

meaningful commercialization, we believe fully autonomous trucks are further away

than would be thought at first glance and that trucks would need to reach level-4 or

5 autonomy on highways and be capable of operating on major interstate freight

routes before they can be truly disruptive.

Introducing the Tech and the Players

We believe it’s important to clearly differentiate between level 3 and level 4

autonomous trucks, as the gap between the two levels is likely to be significant from

an operational perspective. To that end, an overview is provided below.

Figure 119. Overview of Level-3 and Level-4 Autonomous Commercial Trucks

Source: American Trucking Research Institute, Daimler AG, SAE International, U.S. Department of Transportation, Citi Research


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Although autonomous heavy-duty trucks have been used in commercial mining

operations in foreign countries since at least 2008, there is currently not a level 3+

highway-capable autonomous heavy-duty truck available for purchase in the US.

That said, multiple companies are currently developing level 3-4 trucks and

progressing towards full commercialization of at least partially autonomous trucks in

the near future. Companies focused on developing autonomous heavy-duty long-

haul trucks include Daimler, Embark, PACCAR, Starsky Robotics, Kodiak Robotics,

TuSimple, Einride, and Waymo, with most of these companies planning to release

autonomous trucks in the U.S. market. Volvo is an additional company that is worth

highlighting, but it appears more focused on developing autonomous trucks for

operations outside of long-haul (e.g., refuse, mining, short-haul, and agricultural).

Bending the Cost Curve With Autonomous Trucks

We believe the financial benefits achieved by trucking companies operating level 3

– 5 autonomous trucks will fall into four main categories:

1. Safety: Insurance-related cost savings would come from an expected reduction

crash frequency, partially offset by an increase in crash severity (due to more

expensive equipment).

2. Driver Headcount Reductions: Headcount reductions are trucking companies’

largest source of financial savings from autonomous trucks, but in a scenario

where autonomous trucks are widely used by commercial fleets we assume all

remaining drivers are paid more as they likely would perform more specialized

tasks.

3. Fuel: We estimate that automated driving systems will be able to reduce long-

haul trucks’ fuel consumption by 5% through fuel-efficient driving techniques

alone, with potential additional savings coming from tractor design upgrades

and the inclusion of “platooning” technology, which utilizes some autonomous

driving features and requires vehicle-to-vehicle (V2V) technology.

4. Productivity Enhancements: We believe level 4 and level 5 autonomous

trucks are capable of significantly increasing carriers’ capacity, given that they

can theoretically operate up to 24/7 (conditions permitting) if a driver is not in

the truck and remote control is not being used.

Ultimately, after factoring in the estimated incremental operating expenses

associated with the higher cost of an autonomous truck that is capable of platooning

and assuming a longer useful life, we estimate the total annual expense per mile of

a new level 5 class 8 diesel long-haul tractor will be 48% lower than the current

annual expense per mile of a comparable non-autonomous tractor.

Adoption Hurdles for Autonomous Commercial Trucks

Driving a truck is not an easily automatable task, and we believe the path to fully

autonomous trucks reaching authorized operation in interstate freight transport

throughout the country faces multiple hurdles. In our opinion, the greatest hurdle

facing autonomous commercial truck adoption is the current lack of sufficient state

and federal legislation allowing autonomous trucks testing and the lack of federal

legislation governing autonomous truck development/operation, as ultimately

Congress has the power to regulate interstate commerce.


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We believe that using autonomous trucks in a state with laws that don’t explicitly

allow their full operation is an unjustifiably high risk for developers, and the current

lack of legislation/permission is likely limiting testing, as current developers have

only tested trucks with more advanced autonomous driving capabilities in a handful

of states, while the number of states open to initial commercial (or pilot) programs is

even smaller.

In addition to state and federal legislation, other less obvious hurdles are also

present. For example, autonomous driving technology for trucks faces hurdles

associated with the reality of removing a driver from the cab, which makes

monitoring/securing cargo while in transit, ensuring cargo/vehicle safety, and

performing other tasks that are frequently completed by drivers more challenging.

Unions and public perception are also likely to be meaningful hurdles. The

Teamsters exhibited their influence over the autonomous trucking legislative

process by successfully lobbying for the exclusion of vehicles weighing 10,000+ lbs.

from the AV START Act, and we also note that autonomous trucks are likely to have

a more unfavorable public perception than autonomous cars due to their larger size.

Expected Timeline for Adoption

Given the adoption hurdles that we expect autonomous trucks to face, our base

case assumption is that adoption rates will not pick up until the mid-2020s, which is

when we assume federal legislation will be passed that aids autonomous truck

commercialization, as we expect large manufacturers to (for the most part) only

begin selling level 3 or higher trucks after legislation passes. With this timeline, we

do not expect level 3 trucks to ever achieve high rates of adoption in the U.S., due

to our expectation that they will produce relatively limited financial benefits (factoring

in system cost) and the likelihood that autonomous truck development/testing will

have surpassed level 3 trucks by this point. We expect level 4 trucks’ adoption rate

will remain fairly low until the late 2020s, due to time spent on technology

development and building market demand. If federal legislation is not passed by the

mid-2020s, we believe the timeline for level 3 – 5 truck adoption will be extended

accordingly.

Our base case assumes the first level 5 long-haul trucks will be available for

widespread sale by roughly the mid-2030s, following an extensive period of

testing/development and possible federal/state law changes (if needed) that we

expect to begin towards the end of the 2020s. We expect an increase in level 5

trucks’ adoption rate will coincide with a decline in level 4 trucks’ adoption rate, as

buyers replace aging level 4 trucks with level 5 trucks, and expect level 5 trucks to

reach mass adoption by the early-to-mid 2040s.


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Figure 120. Autonomous Long-haul Truck Adoption Rates (Base Case)

Source: Citi Research Estimates

Note: “Adoption rate” measured as percentage of the active US class 8 tractor population (mass adoption is 50%+)

0%

10%

20%

30%

40%

50%

60%

70%

80%

2017 2020 2023 2026 2029 2032 2035 2038 2041 2044

Level 3 Level 4 Level 5


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(Flying) Car of the Future As we have written elsewhere in this report, establishing an integrated mobility

network could usher in the use of airborne vehicles to further revolutionize urban

mobility. A fully networked airborne autonomy solution could provide even greater

congestion relief by pulling people off the ground. And more importantly, it can

shuttle passengers over farther distances, at a faster speed, without regard for land-

based obstacles which can be expensive to build around. And the long-term dream

is that taking more advantage of vertical space can relieve over-population.

It’s going to be decades before we arrive at that future since the “flying car,” let

alone an autonomous one, is even more complicated than its ground-based

counterpart (propulsion technology, regulation and security are massive obstacles).

But, important steps are being taken today that will enable more airborne urban

mobility solutions. For now, those activities are focused on the technology

necessary for airborne autonomy to make economic sense; namely electric

propulsion. Various (mostly smaller) companies are also working on what the actual

vehicle will look like. As is the case in terrestrial AV, it’s an open question as to who

will own the value stack and how best to monetize what will be an opportunity in the

future. There are also reasons why we might be decades away from seeing a

widespread autonomous airborne solution. But there is certainly plenty of white

space when it comes to autonomous aerial vehicles (AAV) for urban mobility.

What Is a Flying Car?

To relegate the “flying car” to the future ignores the fact that we already have

airborne solutions for short distances. The helicopter has been filling this role for

decades. But it’s difficult to fly, hard to maintain, relatively dangerous, noisy, and

expensive. All that means is that the helicopter is inaccessible to the vast majority of

the population. The flying car future is essentially an environment in which more

people can say they’ve ridden in a helicopter than is the case today. There was a

time not long ago when having flown in an airplane was considered a luxury. It’s

now relatively commonplace (at least in developed economies). There are already

companies working on making helicopters more accessible by using booking

platforms to reduce the price point and make helicopters a more common part of the

urban mobility landscape. Blade and VOOM are two examples; both have Airbus

backing/partnership. But at $195 per seat for a 5 minute BLADE ride from

Manhattan to JFK airport, it’s still not cheap. And it’s still using existing vehicles and

infrastructure. So a true AAV urban mobility solution involves multiple parts:

Vehicle: From a simplistic perspective, the helicopter provides a good framework

for an urban mobility solution due to its vertical take-off and landing (VTOL)

capability. VTOL is necessary since urban environments do not afford the space

required for traditional take-offs (you can’t put an airport in a big city). So an

urban mobility solution will likely involve a vehicle with multiple propellers to

achieve vertical take-off and horizontal flight.

– Propulsion: This is probably the key technological obstacle since an AAV will

have to be electric for a variety of reasons including cost and noise reduction.

Unlike terrestrial vehicles, there is not yet a reliable electric propulsion solution

for airborne vehicles that can carry multiple people a useful distance.

Combined need of VTOL and electric means that “eVTOL” tends to be the

buzzword for airborne urban mobility (although there is no one-size fits all).

“Roads? Where we are going we don’t

need roads”

- “Doc” Brown, Back to the Future (1985)


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Infrastructure: In our view, urban AAV will begin with vehicles traveling pre-

determined routes between fixed points or “hub-to-hub.” Regulatory and airspace

management complexities make it difficult to have vehicles taking and landing at

random times in random locations. This requires heliports or “vertiports” capable

of processing vehicles and passengers at relatively high volumes and quick

turnarounds. These could be all-new structures, or enhanced building roofs,

parking lots, and existing helipads. In this environment, ground transportation still

plays a vital role getting people to the “vertiports.”

– Airspace: Unlike terrestrial AVs, AAVs don’t need roads, bridges or tunnels on

which to operate. By that limited definition, the AAV infrastructure is already

built. But managing that infrastructure requires solutions to ensure safety and

security. This could be software and sensors embedded in every vehicle,

ground/space based sensors tracking movements, or likely a combination of

the two. The sky is already very busy and run on relatively old (yet effective)

technology. Putting significantly more things into the sky creates safety

concerns for those in the air and on the ground.

“Pilot”: The most ambitious visions for airborne urban mobility assume the

vehicles will be pilotless. This assumes that machines can make better decisions

than humans (potentially true if the sensors are all working). It also makes the

likely reasonable assumption that there won’t be enough human pilots available

to fully democratize. So why build a pilot-centric system if you are going to run

out of them? However, there are technological, regulatory and psychological

issues that make a truly unmanned aerial vehicle difficult to realize even within

the next decade. As a result, we expect to see pilots in the initial iterations of

urban air mobility. However, the vehicles will very likely already have some

elements that will be needed for autonomy down the line including distributed

power systems, advanced sensors, and fly-by-wire (some of this already exists in

aviation). So over the long-term, the vehicles could be adapted to the

infrastructure to eventually operate without pilots. This is the most common

approach. However, some (like Airbus) are pushing for “direct to autonomy,”

albeit operating in more constrained environments (similar to AVs operating in

level 4 domains). As a result, there is still a debate in urban mobility AAV about

the pros/cons of designing a system around a pilot.

How does airborne AV compare to ground-based AV?

In some ways, airborne AVs are easier to achieve than ground-based AVs:

They operate in a uniform domain: only airplanes are in the air. There aren’t any

traffic lights, roundabouts, potholes, pedestrians. And you don’t have to build any

new rails, roads, tunnels or bridges if you want to go further.

There’s already a lot of autonomy: The uniformity of the operating domain has

allowed the aviation industry to field numerous components needed for autonomy

over the years. Sensors, radars, control mechanisms. For instance, commercial

aircraft already do a lot of the flying themselves (although there are tragic

examples when systems or sensors fail). Aviation also benefits from the

government customer demanding airborne autonomous technology. In that

sense, the autonomous technology is there.


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But in many ways, AAV is harder:

There’s no propulsion solution: AAV might have a leg-up on autonomy

technology, but it’s behind in propulsion. Numerous companies appear to be

making headway with propulsion solutions which would enable a ground-based

autonomous network. The electric part of the VTOL equation is still very much in

development and a critical part of achieving AAV urban mobility.

Regulation: The sky might be a uniform domain, but it’s a busy and highly

regulated. This is especially true in a busy urban environment at relatively low

altitudes. And even if technology could de-conflict the traffic, there are extensive

regulations governing the sky that are difficult to change without extensive

investigation and testing which can take many years, if not decades. Consider

the difficult that the FAA has had regulating the use of remotely-piloted aerial

vehicles and the associated concerns if these vehicles approach restricted

airspace.

Safety: Auto accidents tend to be somewhat contained. Air accidents, especially

those in urban environments, can have wider impacts. As a result, they probably

have a higher bar to clear in terms of ensuring safety and reliability. This also

plays into consumer psychology. The average person might be more willing to

step into a driverless car vs. a pilotless aircraft (escaping a car appears easier

than escaping an aircraft).

Security: In-air safety is obvious, but the safest airborne vehicle is still airborne

which by definition poses a threat to anything on the ground. Relatively limited

and highly regulated urban air transport means it’s not a big problem today. But in

a world of hundreds or thousands of airborne vehicles taking to the sky with

unregulated passengers, we would imagine the Department of Homeland

Security to take interest. And if there are fully autonomous systems, then they

obviously need to be cyber-hardened to avoid nefarious actors from hacking

systems and controlling aircraft which could have potentially catastrophic results

(similar requirement in terrestrial AV).

Who’s Working On It?

Enabling airborne urban mobility is about getting a lot more people traveling through

the air more regularly. In that sense, almost every company involved in aviation

today is interested in contributing to the next technology which could significantly

open up the addressable market for airborne solutions. So we see serious work

being done across companies large and small, new and old. At the end of the day,

anything that enables more aero travel should be good for purveyors of airborne

products and technologies. Of course, developing “flying cars” requires new

development cycles, price-points, and manufacturing scale. We note that this latter

point could mean more cooperation between aerospace and auto sectors. But all of

this suggests the aerospace “incumbents” have opportunity ahead if the air travel

TAM expands dramatically.

This includes aircraft manufacturers (both fixed-wing and helicopter OEMs), aero-

engine manufacturers (key to developing electric propulsion), and

component/avionics providers (important for the piloting sensors and overall aircraft

connectivity). Traditional companies also have more experience with regulatory

bodies. So while new entrants may get a lot of the fanfare, it stands to reason that

the traditional aerospace industry will be involved in this aerospace solution.


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However, the company that gets the most press in this field is not a traditional

aerospace company. Give credit to “Uber Elevate” for publicizing and accelerating

the discussion with its white papers and summits. Convening sharp minds and

generating excitement is critical. They’ve even released a “Common Reference

Model” for the sort of vehicle they would like to see. But let’s be clear: Uber does

not claim to be building a vehicle or the physical infrastructure. Instead, they will run

the network (uberAir) on which eVTOL aircraft will operate. So they have partnered

with several established aerospace firms to develop the aircraft. This is a subtle

continuation of their current business which doesn’t claim to build cars; it networks

them. And similar to the AV conversation, it’s still unclear who will own the value

chain in urban mobility AAV. Do you have different vehicles operating on one

network? Does the vehicle OEM run the entire system?

There are probably 50 to 100 aircraft under development at various stages,

although many still fall in the concept category. Some examples:

A^3 (subsidiary of Airbus): Vahana is a self-piloted eVTOL which completed its

first test flight in February 2018 (reached an altitude of 16 feet over 53

seconds).The concept is unique since it pushes for a “direct to autonomy”

approach arguing that doesn’t make sense to waste weight/space on a pilot.

Aurora (owned by Boeing): They’re developing an eVTOL designed for fully

autonomous operations, but to be initially operated by a “safety pilot” plus two

passengers. It’s designed for hub-to-hub use, with test bed flights scheduled to

begin in 2020 in a few locations worldwide. Aurora is also working with Uber.

Bell (owned by Textron): The decades-old “Bell Helicopter” business recently

rebranded to “Bell” to highlight its role as a broader provider of airborne mobility

solutions. They’re one of a few companies working with Uber on a potential

eVTOL solution. They’ve shown their Urban Air Taxi concept at a variety of

traditional and non-traditional industry events (including SXSW and CES).

Other Uber partners: Embraer, Pipistrel, Mooney/Carter, and Karem all have

eVTOL concepts. These are in addition to Aurora and Bell who are also working

with Uber.

Other startups: Vertical Aerospace plans to launch an air taxi service with

eVTOL by 2022 with pilots. Lilium has an electric VTOL jet. Volocopter has an

eVTOL targeting a series of urban flight tests in 2H19 in Singapore. Joby has an

aircraft they have been working on for almost a decade with funding from

companies including JetBlue and Toyota. Google CEO Larry Page is backing

Kitty Hawk and Opener, two companies with three aircraft projects between them

(the single-pilot Flyer is available for sale). At the 2018 Citi Tokyo Auto

Conference, Kitty Hawk CEO Sebastian Thrun noted that air-tax services would

initially operate on specific routes into major cities. Besides these startups we

believe there are several more startups working on this technology, including in

Israel where aerospace-military technology is being leveraged (startups include

Urban Aeronautics, NFT).

Automakers: Including Porsche, Toyota, and General Motors, as well as others

who are invested in this space (including Daimler/Volocopter, Geely/Terrafugia)


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Component providers will also be critical to the ecosystem, including large

companies like Honeywell or United Technologies providing avionics and advanced

electronics. Defense companies can also participate given their extensive

experience developing autonomous systems. Lockheed Martin’s MATRIX

technology enables autonomy. Northrop Grumman is already a leader in

autonomous systems, and has a distributed aperture system (DAS) which could

help aircraft sense more of what’s around them. Raytheon is also developing a new

DAS for the F-35 fighter jet.

Potential Timeline

Most companies suggest they’ll have “something” in the sky by the early/mid-2020s.

It really varies by company what that “something” is, but the nearest term goal

appears to be a relatively straightforward “air taxi” service but using an electric-

powered VTOL aircraft. This implies certification of an electric aircraft in 2020-23.

However, this isn’t all under industry’s control. In some cases, battery technology

still needs to take a step forward to provide the power necessary to hit a useful

range and lift. And timing will be paced by regulators who have a tendency to move

slowly (and perhaps for good reason). To that end, we could see faster adoption

outside of the United States due to more flexible regulatory bodies.

Uber is probably one of the most aggressive in terms of its desired timing,

suggesting it will have demonstrations in LA and Dallas in 2020 with commercial

flights available by 2023. In our view, the early-2020s seems aggressive given the

technological and regulatory obstacles. The key focus over the next ~5 years will

likely be on battery and propulsion technology to enable the eVTOL model. And in

that time, we could see non-VTOL electric solutions pop up which will help connect

farther-flung hubs more efficiently. And it probably won’t be until well beyond 2030

that we see a true dual-use vehicle that can transition from road to air and back

again.


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Mobility Ecosystem Changes: Implications for Corporate Treasury Few industry sectors are poised for dramatic change as those in the automotive

space. Technology is transforming what was once an industrial and manufacturing-

centered sector into a hotbed of innovation that is at the heart of the emerging

mobility ecosystem. There are numerous trends influencing this transformation but a

select number of drivers — powertrain electrification, ridesharing, autonomous

vehicles, the shift to experiential transportation services, and consumer adoption of

e-commerce — will influence the broader ecosystem.

How is Digital Disruption Impacting the Mobility Ecosystem?

As a result of all of these changes, the mobility ecosystem — consisting of auto

suppliers, auto original equipment manufacturers (OEMs), retailers, after market

service providers, auto finance companies, insurance companies, energy / fuel

companies, connected car services and transport/mobility providers, will realize a

considerable shift in revenues.

Figure 121. Changing Value Chain – New Entrants, New Business Models, New Capabilities, Deeper Connectivity

Source: Citi GDS analysis, Deloitte - Future of Mobility: : How transportation technology and social trends are creating a new business ecosystem, WEF& Accenture – Unlocking B2B value

Suppliers, OEMs, transport/mobility, insurance, and connected services will likely be

the most impacted by digital disruption.


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Suppliers: Traditional auto suppliers are moving from selling physical parts to

selling specs, as 3D printing enables printing parts on the factory floor. Supply

chains are also getting connected, leading to greater just-in-time procurement.

Traditional suppliers are likely to lose considerable revenues, as much as 22

percent according to Citi GDS analysis due to the growth of technology suppliers.

OEMs: Major OEMs are exploring new business models — ridesharing,

subscription based models, as new entrants are changing the market dynamics.

As personal car ownership reduces with the growth of ride hailing services in

developed markets, OEM sales will be driven by fleet customers and we believe

emerging markets will become the driver of personal car sales growth.

Insurance: The rise connected and autonomous car sales and growth of ride-

hailing services means insurance products are increasingly going to be being

tailored to suit the changing mobility landscape. Insurance is already shifting from

individual to technology components, micro insurance products are seeing

greater traction, and insurance is increasingly being bundled at point of sale,

which is likely to cause a decline in insurance value pools.

Transport/Mobility: Transport/mobility service providers will focus more on the

consumer’s need to move from point A to point B in the most efficient way —

through ridesharing, ride hailing, and multi-modal transportation — rather than

individual car ownership. This new sector is expected to see rapid growth

through over the coming decade with expectations of gaining a 9% market share

in the transport vertical by 2030. Mobility services will also likely have a

significant impact on the payments landscape, triggering the miniaturization of

payments.

Connected Services: Connected cars are changing the way consumers

consume media, music, e-commerce, and related services. Such services will

provide new revenue streams for OEMs, as the services are increasingly

embedded into the car platform (i.e. the ‘Mercedes me’ online platform offered by

Mercedes-Benz).

We see the changes happening across the mobility ecosystem resulting in the

emergence of four key themes, which are likely to have great implications on future

trading models and associated cash flows.

1. New Distribution/Supply Counterparties: The rise of connected cars,

autonomous vehicles, and design innovation driven by technological

differentiation is resulting in technology companies establishing themselves as

key suppliers to auto OEMs. This is likely to result in a power shift from

traditional component suppliers to newer technology vendors (i.e. Apple and

Google on the Software side and Panasonic on the battery and powertrain

side).

On the distribution front, new channels such as online marketplaces are

disrupting the role of dealers. In 2016. Amazon Vehicles launched a new hub

for car buyers — aka the “automotive community” — providing users features

such as car comparisons and car parts and accessories shopping. The growth

of ride hailing models, are also likely to impact distribution as car sales to fleet

operators will replace sales to individual owners, specifically in developed

markets.


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2. Direct-to-Consumer Model: OEMs are embracing direct-to-consumer sales as

consumers get more comfortable with purchasing vehicles online. One example

is Peugeot, which launched their online car sales service in early 2017 which

also includes online auto financing. Hyundai and Smart have also launched

buying portals and we believe direct-to-consumer models can help OEMs

reduce costs and drive profitability.

3. Shift to Services: As described in earlier sections, we see the automotive

industry experiencing a paradigm shift in how a consumer interacts with an

auto in the future — a shift from vehicle ownership to vehicle usage. The focus

and investment of OEMs in the ride hailing market will likely accelerate as

these companies look to protect against disintermediation.

Further, the Car of the Future will not merely be a transport vehicle but a

platform providing a seamless user experience with a plethora of services

including open-source infotainment, connected-car commerce, and public

infrastructure services (e.g. toll and parking). The World Economic Forum

predicts that OEM-driven applications and services will contribute $14 billion in

value creation as connected cars grow from approximately 23 percent of the

market in 2016 to roughly 70 percent of the market in 2025.

4. Data Monetization Opportunities: With autonomous vehicles generating

approximately 4 terabytes of data in an hour and a half of driving, OEMs will be

able to capture huge amounts of consumer data, such as driving behavior and

buying patterns. This data can potentially lead to new revenue streams from

third-parties like insurance providers and parts manufacturers. Likewise,

predictive maintenance is expected to help fuel the after-sale market, while

improving data flow within the supply chain.

Emerging Priorities Due to Radically Changing Cash Flows and Corporate Treasury Reaction

Changes in the trading model and associated cash flows present new challenges

for treasury organizations, necessitating treasury to play a more strategic role.

Hence, treasuries need to become embedded with businesses at a much earlier

stage to influence product decision (e.g., account management, payments,

collections, refunds, facilitative direct-to-consumer business models). With new

business models and innovation, there is also increased complexity, newer risks,

and increased responsibilities for the treasuries.


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Figure 122. Possible Future OEM Trading Model: New Flows with Radically Different Characteristics, New Potential Risks to Manage

Source: Citi GDS Analysis, Citi Auto SME Interviews

New Distribution / Supply Counterparties

With increased car sales to fleet operators, treasury can expect a rise in Business-

to-Business (B2B) flows. On the supply side, technology suppliers will gain

prominence, and will likely have greater financial leverage on the OEMs. However,

OEMs might face shortened days payable outstanding (DPO) challenges due to

new suppliers and relationships.

The increased buying power of fleet owners may also result in less favorable

payment terms for OEMs, creating high days-sales-outstanding (DSO) challenges

and hence cash deficits in day-to-day operations. OEM treasuries will want to focus

on working capital financing to handle expanded DSOs and shortened DPOs while

also managing counterparty risks.

Direct-to-Consumer Model

Direct-to-consumer models will bring in new Consumer-to-Business (C2B) real time

flows that are bypassing the traditional dealers. Ridesharing models will shrink the

nature of cash flows to small/micro value levels as compared to traditional car sales.

As personal car sales growth accelerates in emerging markets and direct-to-

consumer sales become prominent, there will also be implications on non-G10

currency flows.

It is imperative for treasuries to build global direct-to-consumer collection

capabilities with growing direct-to-consumer sales. Treasuries need to increasingly

focus on risks that arise out of new currency flows, managing foreign exchange risk,

and foreign exchange currency spread, in response to market changes.


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Shift to Services

As connected car services continue to grow in popularity, they will generate micro-

value, high-volume, real-time, subscription, and e-commerce flows. Increased cash

flows from insurance and maintenance-as-a-service business models can result in

early cash collections in the form of premiums and subscription feed, necessitating

streamlined and automated cash management capabilities. On the other hand,

OEMs will also need to manage liquidity to pay out to drivers, vendors, and third-

party providers.

As connected car services continue to grow in popularity and tailored subscription-

based solutions develop, OEM treasuries should consider developing global direct-

to-consumer collections and reconciliation capabilities to handle increasing micro-

value, high volume, real-time, subscription, and e-commerce flows. OEM treasuries

should also focus on working capital management in light of the fact that vehicles

will continue to reside on their books and thus, it may take longer for OEMs to

recoup manufacturing costs over the life of the connected car’s services.

Data Monetization Opportunities

Revenues associated with data monetization will gain prominence, for both

suppliers and OEMs. With large numbers of datasets created from the connected

car environment, OEMs and suppliers will be able to sell data to third parties (e.g.

ad agencies, local governments).

Treasurers need to build capabilities to handle and monetize data. It will also be

imperative for OEM treasurers to establish data revenue sharing agreements with

their suppliers.

In summary, as the auto industry continues its transformation into the “mobility

ecosystem” there will be significant impacts on auto company treasury operations.

These include the need to develop capabilities to handle global real-time direct-to-

consumer collections and reconciliations, address working capital management

challenges arising out of the shift in ownership patterns and emergence of newer

suppliers and distributors and actively seek out newer financing opportunities with

new supply / distribution counterparties.


© 2018 Citigroup

144

Figure 123. Key Implications on Corporate Treasury

Source: Citi Trade & Treasury Solutions

Citi Global Perspectives & Solutions (Citi GPS) is designed to help our clients navigate the global economy’s most demanding challenges, identify future themes and trends, and help our clients profit in a fast-changing and interconnected world. Citi GPS accesses the best elements of our global conversation and harvests the thought leadership of a wide range of senior professionals across the firm. All Citi GPS reports are available on our website www.citi.com/citigps

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© 2018 Citigroup

147

Notes:


© 2018 Citigroup

148

Notes:


© 2018 Citigroup

149

IMPORTANT DISCLOSURES

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© 2018 Citigroup

151

NOW / NEXT Key Insights regarding the future of Mobility

INNOVATION Today, automakers are forced to guesstimate what features consumers want to buy

in their new car and consumers are forced to make a decision at the time of

purchase and may not know what features they want to buy. / A new subscription

model would see automakers offering autonomous packages essentially at cost

while deriving profit from subscriptions to driving services that are turned on later

through an over-the-air update.

SHIFTING WEALTH The majority of autos todays are either owned or leased by consumers through a

dealer network. / In the future, consumers in urban and suburban areas are more

likely to use either RoboTaxi’s or join an AV Subscription network to get from point

A to point B.

TECHNOLOGY Most ADAS regulation in recent years has focused on automatic emergency braking

and to a lesser extent lane departure warnings and ADAS is gradually becoming

standard issue. / ADAS 2.0 will involve a wider sensing coverage perspective,

superior sensing coverage and increasingly demanding software.

Citi GPS: Global Perspectives & Solutions © 2018 Citigroup

www.citi.com/citigps

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