www.ptvgroup.com

SIMULATING AUTONOMOUS VEHICLES ON

OUR TRANSPORT NETWORKS

Alastair Evanson,

Solution Director PTV Vissim

www.ptvgroup.com I Slide 2

TOMORROW‘S BUSINESS MODEL: SIGNIFICANT SHIFT TO SHARED MOBILITY CONNECTED & AUTONOMOUS VEHICLES

LEVEL 2:

PARTIAL

AUTOMATION

LEVEL 3:

CONDITIONAL

AUTOMATION

LEVEL 4:

HIGH

AUTOMATION

LEVEL 5:

FULL

AUTOMATION

www.ptvgroup.com I Slide 3

TOMORROW‘S BUSINESS MODEL: SIGNIFICANT SHIFT TO SHARED MOBILITY KEY QUESTIONS MOVING TOWARDS A CAV FUTURE

WHAT ARE THE KEY QUESTIONS AROUND THE INTRODUCTION OF CAV’S IN CITIES?

Potential for increase in kilometres travelled

Impact of varying AV rates of

penetration across ‘mixed’

traffic

How will AV’s be

programmed to drive?

What levels of infrastructure

alterations are required, who

will provide the investment? Who will provide the

required regulation?

How will various ride-share,

CAV’s operating systems

within a city?

How will kerbside pick up

and drop off operate?

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WHAT DO YOU WANT TO TEST?

Impact of AV’s on highway

speed flow relationship?

What about intersection

capacity and saturation flow?

Which mode of transport has

the biggest impact?

Will dedicated CAV

infrastructure provide a

significant benefit?

How many CAV’s create a

‘tipping point’ in the network?

Will they communicate with

infrastructure?

What will the headway to other

vehicles be in all situations?

What is the acceleration and

deceleration profile?

Will the vehicle form a platoon?

How to behave if the next

vehicle isn’t an CAV?

What if the vehicle in front has

less deceleration power?

Vehicle Behaviour Test

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TOMORROW‘S BUSINESS MODEL: SIGNIFICANT SHIFT TO SHARED MOBILITY IMPORTANCE OF SCENARIO LED MODELLING

We know CAV’s are coming but often lack detailed

information on the characteristics of their behaviour.

Scenario testing allows broader understanding of the

range in impacts from CAV behaviours.

Scenarios can be related to behaviours of vehicles or

penetration rates.

Abstract networks, test tracks or existing infrastructure.

Output results should be used to test range or tolerance

of designs ensuring that future resilience and flexibility

is included.

Is it reasonable to continue to design for 30year

horizons?

Example:

• Changes in headway affecting capacity;

• Changes in penetration of AV’s affecting capacity.

www.ptvgroup.com I Slide 6

TOMORROW‘S BUSINESS MODEL: SIGNIFICANT SHIFT TO SHARED MOBILITY THE BENEFIT OF TRANSPORT MODELLING & SIMULATION

A Virtual Environment to answer operational strategy

questions:

Testing CAV & MaaS vehicles impact on traffic

Technology testing by running software & hardware

in the loop.

Cost effective compared to real world test bed.

Flexible to quickly undertake and assess unlimited

scenarios.

Determine acceptable CAV operation for

deployment

Creating a platform for roundtable discussions between:

Vehicle Manufacturers;

Technology Suppliers;

Infrastructure Designers;

Transport Operators.

www.ptvgroup.com I Slide 7

PTV VISSIM & CONNECTED AUTONOMOUS VEHICLES

HOW TO MODEL CONNECTED AUTONOMOUS VEHICLES WITH PTV

VISSIM?

Internally:

‘Adapting default driving behaviour parameters’

Car following model Lane change behaviour

Speeds

www.ptvgroup.com I Slide 8

PTV VISSIM & CONNECTED AUTONOMOUS VEHICLES

Externally:

‘Using one of PTV Vissims interfaces’

DriverModel.dll

DrivingSimulator.dll

COM Interface

HOW TO MODEL AUTONOMOUS VEHICLES WITH PTV VISSIM?

Internally:

‘Adapting default driving behaviour parameters’

Car following model Lane change behaviour

Speeds

www.ptvgroup.com I Slide 9

PTV VISSIM – EXTERNAL DRIVER MODEL .DLL

Replaces ‘internal’ driving behavior with a user defined one.

Can be activated for different vehicle types, allowing for some all

vehicles to be controlled in the simulation run.

PTV Vissim passes state of vehicle & surrounding to the .dll for each

individual time step.

.dll computes the ‘reaction’, i.e. acceleration / deceleration and lateral

behaviour of the vehicles from user defined parameters.

.dll passes information back to PTV Vissim for the next time step.

www.ptvgroup.com I Slide 10

PTV VISSIM – DRIVING SIMULATOR INTERFACE .DLL

Driving

Simulator

DLL

Calculate

new position

of driver

vehicles for

next

Simulation

step

Vissim

Simulation

step of all

vehicles in

the network

Start Vissim

Get Traffic Vehicles & Signal States VehID, VehType, Position (x,y,z), Orientation, Speed, Leading & Trailing Vehicle,

LinkID, LinkName, LinkCoordinate, LaneIndex,TurningIndicator

SignalGroupID, SignalState, ControllerID

Set Driver Vehicles & Detection Position (x,y,z), Speed, Orientation, Activates detectors

During Simulation Run:

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CETRAN SINGAPORE

Centre of Excellence for Testing and Research of Autonomous Vehicles - NTU

Launched by the LTA in August 2016 in partnership with NTU

1.8 ha CETRAN Test Circuit with a simulated road environment for testing AV’s prior to their deployment

on public roads.

Testing in a computer simulated environment representative of Singapore‘s traffic conditions, to

completement the tests performed in the test circuit.

Goal:

Conduct research towards standards and test procedures to ensure safety and security of

autonomous vehicles to enable deployment on Singapore public roads.

www.ptvgroup.com I Slide 12

CETRAN TEST CIRCUIT

The straight Asses performance on straight and empty section of road – ensure vehicle does not show “road hogging” tendencies

Rain Simulator Rain generator to test AV performance in tropical rain conditions

Bus bay Singapore style bus bay with yellow “give” way box to test give way rules in relation to public transport

Carpark Gantry Car park gantry to test entry to and exit from HDB estates to simulate passenger pickup from HDB Flats

Roundabout Test of give way rules on roundabouts

Raised pedestrian crossing Test of speed hum detection and zebra crossing detection on crossings as seen in HDB estates

S-Course Test AV maneuvering capability in tight spaces

Carpark Carpark space – to test behavior of extended wait for a passenger at a HDB pickup point

Un-signaled

intersection Test management of an intersection without traffic light but with yellow box

Turning lane Handling of multi-lane intersection and selection of correct lane.

Slope Test of slope and handling of reduced visibility on crest

Small speed hump Detection of and handling of small speed hump

Signaled intersection Handling of signaled intersection and zebra crossings - assessment of correct prioritization of these intersections

Bus lane Correct handling of Bus Lane as seen in Singapore.

Workshop Workshop to prepare vehicles and test equipment for use on circuit

Smart Mobility Network Extension of NTU Smart Mobility Network to test vehicle to infrastructure communications and to support test equipment and high accuracy positioning.

www.ptvgroup.com I Slide 13

COMPUTER SIMULATED ENVIRONMENT

Safety Case Generation

Safety Case Evaluation

Safety Case Verification

Safety Case Generation:

Creation of a safety case database of cases to be considered on Singapore Roads

Initially about 40,000 cases growing over time to about >500,000 cases

Safety Case Evaluation:

Evaluation of these cases and bring them down to about 1,000 – 10,000 cases to be tested through

simulation

Safety Case Verification:

Verification of simulation test with about 100-200 physical tests on the test circuit

www.ptvgroup.com I Slide 14

INTEGRATED SIMULATOR

Control Algorithm

for AVs (MATLAB)

Autonomous Vehicle

Kinematic Model

Traffic Simulation (VISSIM)

Sensor Simulation (PreScan)

Relative measurements and sensor data

Autonomous vehicle states

Network simulation metrics

Connected via VISSIM’s Driver Simulator

interface .DLL Coded in MATLAB/C++

Communication Simulation

(NS3)

www.ptvgroup.com I Slide 15

SUMMARY & CONCLUSIONS

Micro-simulation modelling is important as a virtual

testbed to analyze the performance, behavior and

impact of CAV’s.

PTV Vissim includes a number of key features to

support CAV testing:

Detailed driving behavior/ interaction models;

Scenario Management;

V2V & V2I capabilities;

COM & UDA’s; and

API’s for external vehicle control.

CAV behavior needs to be defined in the simulation

model based on assumptions or known behavior.

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