SCALABLE, SAFE UND MULTI-OEM CAPABLE ARCHITECTURE FOR AUTONOMOUS DRIVING.

9th Vector Congress | Simon FürstStuttgart | 21-Nov-2018

WE BELIEVE ONLY A FEW PLATFORMS WILL SURVIVE THE RACE FOR AUTONOMOUS DRIVING. THIS IS JUST ONE REASON WHY WE AIM FOR A MULTI-OEM PLATFORM.

9th Vector Congress | Simon Fürst | Scalable Architecture for Autonomous Driving | 21-Nov-2018

PAST FUTURE

Within just a few years only two operating systems for smartphones got established in the market: Android & iOS. All others died out!

Just as in the smartphone operating system market, only a few Autonomous Driving Platforms will succeed.

BMW TACKLES AUTONOMOUS DRIVING CHALLENGES WITH STRONG PARTNERS.

9th Vector Congress | Simon Fürst | Scalable Architecture for Autonomous Driving | 21-Nov-2018

Leading Technology Partners

• More than 100 years of automotive design and production.• Premium ADAS customer experience.• Advanced vehicle electronics integration.

• More than 4.7 million vehicles per year.• Advanced vehicle electronics integration and redundancy

architecture capabilities.• Strong US and European footprint.

• #1 semiconductor manufacturer.• Broad support for OS and safety.

• Leading automotive computer vision technology provider.• Extensive AI expertise.

Leading Integration Partners

• Specialized know-how within Automotive industry.• Reliable system integration partners.• International footprint with high standard.• State of the Art technology providers (e.g. LiDAR)

Leading Automotive OEMs

Resources

Team Technology

Market Relevance

State of the Art

ConceptProduct

Focus

A SCALABLE, SAFE UND MULTI-OEM CAPABLE ARCHITECTURE NEEDS TO INTEGRATE THE FOLLOWING MAIN CHALLENGES OF AUTONOMOUS DRIVING.

9th Vector Congress | Simon Fürst | Scalable Architecture for Autonomous Driving | 21-Nov-2018

HAR

DWAR

E &

INTE

RFAC

ES Motion Control, Odometry

High Performance and safe automotive silicon.

SOFT

WAR

E Driving PolicyPrediction Environment Model

DATAData Center & Big Data Processing

Al training andAlgorithm Evaluation

Data Collection

360° Sensor perception, HD-Map, Localization

COMMON SCALABLE SENSOR CONCEPT MAXIMIZES VALIDATION SYNERGIES.

Short Range Radar Side

6 UltrasonicSensors

Highway Pilot Level 3

Urban Pilot Level 4/5

6 UltrasonicSensors

Surround View Cam Surround View Cam

SurroundView Cam

SurroundView Cam

Front-View CameraFront Radar

Short Range Radar Side

Short Range Radar Side

Short Range Radar Side

LIDAR Front

Far-Range Front-ViewCamera

Short RangeSide Radar

Short RangeSide Radar

Rear Radar

Rear Radar

Rear Driving Camera

Extended Front-ViewCamera for Urban

Driving CamSide Rear

Driving CamSide Front

Driving CamSide Rear

Driving CamSide Front

LIDAR Side

LIDAR Side

LIDAR Rear

LIDAR Rear

RadarLIDARCamera

ADASLevel2

9th Vector Congress | Simon Fürst | Scalable Architecture for Autonomous Driving | 21-Nov-2018

A MULTI OEM API CONNECTS THE COMMON SW-STACK WITH THE OEM SPECIFICSOFTWARE AND HARDWARE.

9th Vector Congress | Simon Fürst | Scalable Architecture for Autonomous Driving | 21-Nov-2018

Vehicle dynamics integration platform

nominal integration platform

FAD

HAD

ADAS cam front

side radars

front radar

Rear radars

Side radars

LidarSide/Rear

NavigationECU HD Map

Cams side

Lidar Front

OnboardOffboard

TelematicsECU

Test fleet Backend for Customer Cars incl. HD Map

Learning Map

Image-/Sensordata

Motion Control

EgoMotion

Cams parking

Cams Front+rear

Steering

Brake

Engine

RedundantSteering

RedundantBrake

Ultra sonic

driver cam

Interface and

toolchain

Middleware, Operating system

Reference design / performance µP

Middleware, Operating system

Reference design / performance µP

Com

pute

r Vis

ion

Sele

ctor

Grid +ML Fusion

HD MapRepresentation

Motion Control fail-operational

Trajectory Following Controller

2nd ASIL Channel

Primary ASIL Channel

Fail degradad integration platform

Development Backend incl. ML und Simulation

Fusion Driving Policy Validator

Fusion Driving Policy Validator

Fusion Driving Policy

L2 SW-stack is used asL3 fail degradion stack

Common SW-Stack OEM-specific SW

Traj

ecto

ry F

ollo

win

gCo

ntro

ller

BMWS SAFETY VISION FOR AUTONOMOUS DRIVING.

9th Vector Congress | Simon Fürst | Scalable Architecture for Autonomous Driving | 21-Nov-2018

Targeting for maximum safety and significant improvements compared to today‘s average human drivers. • Avoid accidents under any circumstances, no matter who is responsible for causing the accident.• If accident is unavoidable, minimize human severity.

To achieve this vision we are developing a scalable safety concept for Highway-Pilot (L3) and Urban Pilot (L4/5) applications, together with strong and safety-oriented OEMs, Tier1s and Technology Partners.

Effects of automation on the driver need to be taken into account

THE TEN SAFETY COMMANDMENTS FOR AUTONOMOUS DRIVING.

9th Vector Congress | Simon Fürst | Scalable Architecture for Autonomous Driving | 21-Nov-2018

Driver‘s responsibilities: Driver monitoring and clear mode awareness necessary

Since the system is responsible to fulfil the driving task, functional safety requires redundancy

If system limits are reached the system need to minimize those risks or performs a safe TOR with sufficient time

Behavior of the system in traffic needs to be predictable. All valid traffic rules need to be taken into account

Security: Protection against manipulation of the system is mandatory

Driver initiated activation and deactivation needs explicit driver intend

If the driver does not respond to a system initiated TOR a minimum risk maneuver has to be performed

Multi stage validation including simulation (e.g. Pegasus: 240 Mio km) is necessary to ensure that the safety goals are met

Data recording while the system is active is necessary to recognize and document unusual events

TOR: Take over request

FUNCTIONAL SAFETY FOCUS FOR HIGHWAY PILOT AND URBAN PILOT.

9th Vector Congress | Simon Fürst | Scalable Architecture for Autonomous Driving | 21-Nov-2018

ASIL D

ASIL B

Fusion of LIDARs, RADARs and cameras with a range up to 300m

Safe and high precision positioning

Safe Localization

HD-Map

Safe HMI for a clear mode awareness

Road condition previewIncluding hazard warning

Emergency stop assistantMinimum risk maneuver

Highway-Pilot and Urban Pilot details see next page

ASIL C

ASIL QM

Safety Features like Automatic Emergency Braking

Teleoperation of vehicle

Fail-degraded ECU

Nominal ECU

Selector + Trajectory Following Controller

Cameras

2nd ASIL channel

Fail-degraded Electrical Network

Main Electrical Network

SAFE Fail-degraded Channel

Validator

ASIL D

Trajectory Following Control + Motion Control

Motion Control

Vehicle dynamics ECU

ASIL BFUNCTIONAL SAFETY CONCEPT IS PREDICTIVE AND AGNOSTIC TO HARDWARE AND MAP. IT IS APDAPTABLE TO ANY SOFTWARESTACK.

Main Channel

Driving PolicyPerception

ValidatorRadars

Cameras

Radars

Lidars

LocalizationHD MAP

Driving PolicyPerception

Prediction

9th Vector Congress | Simon Fürst | Scalable Architecture for Autonomous Driving | 21-Nov-2018

Page 11Subject | Department | Date

THE CHALLENGEXPAD ECU FAMILY - FACTS AND FIGURES

4 Different SOC architectures Infineon AURIX, Intel Denverton, Intel Xeon, MobilEye EQ5 (MIPS)3 Internal software suppliers EV, EF, JC 5 External Software Suppliers: Intel / MobilEye Aptiv N.N. (Adaptive AUTOSAR) N.N. (Safe Linux) N.N. (Supplier for GNSS positioning engine)25 software images (25 diagnostic addresses) 2 mPAD 7 hPAD 16 uPAD

21 Adaptive AUTOSAR images!

Page 13Subject | Department | Date

AUTOSAR AT BMWSP2021 CONCEPT CHANGES IN BMW SYSTEM SOFTWARE

Seite 13

BMW system software components will be redesigned in order to support different API requirements They split into a generic part and a platform specific adapter

Application Mode BAC 4 SP2021 ABAC SP2021 GenIVI in MGU in 2022

GENIVI ADAPTERSTDDIAG

GENERIC PARTSTDDIAG

ADAPTIVE AUTOSAR ADAPTERSTDDIAG

CLASSIC AUTOSAR ADAPTERSTDDIAG

Exam

ple:

BM

W S

tdDi

ag

MAIN BENEFITS OF SAFETY CONCEPT FOR AUTONOMOUS DRIVING.

Benefits of collaboration on safety concept Underlying concept

Automotive grade safety concept and transparent implementation approach for partners and regulators. White box development and shared code basis.

Maximizing availability of AD feature in diverse traffic environments of different regions of the world.

Permanent analysis and validation of the planned driving actions.

Combining LIDAR, RADAR and camera to achieve maximum advantages by the following multi sensor fusion.

Functional safety for AD features Minimization of common cause failures through hardware and software diversity.

Increased flexibility to integrate and combine any ASIL B platform software Hardware, Software and Map agnostic approach.

9th Vector Congress | Simon Fürst | Scalable Architecture for Autonomous Driving | 21-Nov-2018

THANK YOU FOR YOUR ATTENTION. LOOKING FORWARD ON YOUR FEEDBACK AND QUESTIONS.

9th Vector Congress | Simon Fürst | Scalable Architecture for Autonomous Driving | 21-Nov-2018