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
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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.
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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
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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
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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.
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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.
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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.
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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
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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)
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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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