SIMULATING AUTONOMOUS VEHICLES ON OUR TRANSPORT NETWORKS

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1 SIMULATING AUTONOMOUS VEHICLES ON OUR TRANSPORT NETWORKS Alastair Evanson, Solution Director PTV Vissim

2 TOMORROW S CONNECTED & BUSINESS AUTONOMOUS MODEL: VEHICLES SIGNIFICANT SHIFT TO SHARED MOBILITY LEVEL 2: PARTIAL AUTOMATION LEVEL 3: CONDITIONAL AUTOMATION LEVEL 4: HIGH AUTOMATION LEVEL 5: FULL AUTOMATION I Slide 2

3 TOMORROW S KEY QUESTIONS BUSINESS MOVING MODEL: TOWARDS SIGNIFICANT A CAV FUTURE SHIFT TO SHARED MOBILITY Potential for increase in kilometres travelled How will AV s be programmed to drive? Impact of varying AV rates of penetration across mixed traffic WHAT ARE THE KEY QUESTIONS AROUND THE INTRODUCTION OF CAV S IN CITIES? 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? I Slide 3

4 WHAT DO YOU WANT TO TEST? Test Vehicle Behaviour 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? I Slide 4

5 TOMORROW S IMPORTANCE OF BUSINESS SCENARIO MODEL: LED SIGNIFICANT MODELLING SHIFT TO SHARED MOBILITY 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. I Slide 5

6 TOMORROW S THE BENEFIT OF BUSINESS TRANSPORT MODEL: MODELLING SIGNIFICANT & SIMULATION SHIFT TO SHARED MOBILITY 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. I Slide 6

7 PTV VISSIM & CONNECTED AUTONOMOUS VEHICLES Car following model Lane change behaviour Internally: Adapting default driving behaviour parameters Speeds HOW TO MODEL CONNECTED AUTONOMOUS VEHICLES WITH PTV VISSIM? I Slide 7

8 PTV VISSIM & CONNECTED AUTONOMOUS VEHICLES Car following model Lane change behaviour Internally: Adapting default driving behaviour parameters Speeds HOW TO MODEL AUTONOMOUS VEHICLES WITH PTV VISSIM? DriverModel.dll Externally: Using one of PTV Vissims interfaces DrivingSimulator.dll COM Interface I Slide 8

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. I Slide 9

10 PTV VISSIM DRIVING SIMULATOR INTERFACE.DLL Start Vissim Driving Simulator DLL Calculate new position of driver vehicles for next Simulation step During Simulation Run: Set Driver Vehicles & Detection Position (x,y,z), Speed, Orientation, Activates detectors Get Traffic Vehicles & Signal States VehID, VehType, Position (x,y,z), Orientation, Speed, Leading & Trailing Vehicle, LinkID, LinkName, LinkCoordinate, LaneIndex,TurningIndicator SignalGroupID, SignalState, ControllerID Vissim Simulation step of all vehicles in the network I Slide 10

11 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. I Slide 11

12 CETRAN TEST CIRCUIT Turning lane Handling of multilane intersection and selection of correct lane. Slope Test of slope and handling of reduced visibility on crest Un-signaled intersection Test management of an intersection without traffic light but with yellow box Bus bay Singapore style bus bay with yellow give way box to test give way rules in relation to public transport 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. Small speed hump Detection of and handling of small speed hump Raised pedestrian crossing Test of speed hum detection and zebra crossing detection on crossings as seen in HDB estates Roundabout Test of give way rules on roundabouts Carpark Gantry Car park gantry to test entry to and exit from HDB estates to simulate passenger pickup from HDB Flats Rain Simulator Rain generator to test AV performance in tropical rain conditions 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 The straight Asses performance on straight and empty section of road ensure vehicle does not show road hogging tendencies I Slide 12

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 physical tests on the test circuit I Slide 13

14 INTEGRATED SIMULATOR Network simulation metrics Communication Simulation (NS3) Autonomous vehicle states Control Algorithm for AVs (MATLAB) Autonomous Vehicle Kinematic Model Traffic Simulation (VISSIM) Sensor Simulation (PreScan) Relative measurements and sensor data Coded in MATLAB/C++ Connected via VISSIM s Driver Simulator interface.dll I Slide 14

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. I Slide 15

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