Handling Uncertainties in Vehicle Fleet Composition Simulation

1. An approach for handling uncertainties related to the composition of vehicle fleets in traffic simulation experiments with automated vehicles Johan Olstam, Fredrik Johansson, Peter Sukennik

2. Agenda • Background & Aim • Overview of approaches for simulation of automated vehicles • Challenges for traffic simulations of AVs • A way to deal with heterogeneity of AV behavior • A way to deal with uncertainties w.r.t. evolution of AVs • Short on implementation in Vissim • Conclusions and next steps

3. Classification of automated vehicles

4. Large expectations, but deployment of automated vehicles • will not develop in a perfectly linear transition phase to 100% penetration, • will require sharing roads among diversely equipped road users (probably for a long time), • will be the result of technological progress, market development, regulation, and urban mobility policy making.

5. Aim How can we investigate AVs impact on traffic performance in a sound and comprehensive way? Traffic simulation should be able to give answers But how?

6. Summary of state-of-the-art of traffic simulations of AVs • Most simulations considers separate AV-functions as ACC or CACC • Most simulations assumes that all AVs behave the same • Some simulations consider different types of AVs but most of them do not consider that the different types of AVs might coexist • Most simulation experiments focus on motorways or multi lane roads without interaction with active modes • Large variation in traffic performance estimations from previous simulation investigations

7. Different approaches for simulation of automated vehicles • Simulation of automated driving logic by adjustment of behavioural model parameters in the traffic simulation model. • Replacing behavioural models in the traffic simulation model with automated vehicle driving logic models • Extending the driving behavioural models with “nanoscopic” modelling of automated vehicles, including simulation of sensors, vehicle dynamics and driving logics.

8. Challenges for simulation of AVs • Limited data on first generation AVs • No data on future AVs • Transition towards full automation will be long  Large uncertainties w.r.t. • Driving behaviour of AVs • Evolution of AV technology and penetration rates • Behaviour of other road users in response to AVs

9. How to handle the uncertainties? • Scenarios with consistent assumptions • Conceptual modelling of AV capabilities wrt perception, anticipation, driving logic rather than detailed modelling of a specific AV-function • Sensitivity analysis

10. Stages of coexistence • Introductory: • Conventional vehicles still in majority. • Automated driving significantly constrained by limitations (real or perceived) in the technology. • Established: • Automated driving established as an important mode in some areas. • Conventional driving still dominates some areas due to limitations (real or perceived) in the technology. • Prevalent: • Automated driving is the norm, • but conventional driving is still present.

11. Hierarchical specification of AV driving behaviour The level of automation is specified in two steps: • AV class • Basic • Intermediate • Advanced • Driving logic (for different road environments) • Rail-safe • Cautious • Normal • All-knowing

12. AV classes • Basic: • SD only in one directional traffic with physical separation to active modes. • No dedicated devices for vehicle communication and cooperating functions. • Intermediate: • SD in structured traffic. • May have dedicated devices for vehicle communication and cooperating functions, but are not depended on them. • Advanced: • SD in most environments • Will have dedicated devices for vehicle communication and cooperating functions, but are not depended on them.

13. Driving logics • Rail-safe: Based on the switch principle. Follows pre-defined path. • Cautious: Require large gaps; slows down every time its sensors can have blind angles. • Normal: Similar to a human driver but with the augmented (and/or diminished) perception due to sensors. • All-knowing: Perfect perception and prediction of the behaviour of other road users. Capable of offensive driving whenever needed, without causing accidents.

14. Relation AV-class and Driving logics for different Operational Design Domains

15. Example of penetration rates & shares

16. Implementation in traffic simulation • Implementation was done for Vissim • Based on the description of each driving logic we assessed whether the behavioral model parameters are likely to be unaffected, decrease or increase • The Brick wall stop distance principle was implemented for the Rail safe and Cautious driving logics • Varying perception of number of surrounding vehicles • “Calibration” of some of the behavioral parameters were conducted based on field tests with three ACC/CACC vehicles.

17. Example from Implementation in Vissim

18. Implementation in Vissim (cont’d) • Explicit stochastics: Variance in driver behaviour parameter distribution, e.g. desired/maximum acceleration/deceleration • Implicit stochastics: Vissim-”internal” stochastic variation that is meant to model the imperfection of human drivers • safety distances • desired acceleration/deceleration • estimation uncertainty for braking decisions

19. Conclusions and next steps • Conceptual descriptions of different types of AV have been developed and implemented in a traffic simulation model • Consistent scenarios with respect to technological development, deployment • The developed approach will be applied for 5 different use cases around Europe

20. Get in touch! www.h2020-CoEXist.eu Johan Olstam johan.olstam@vti.se OR CoEXist Coordination: Wolfgang Backhaus w.backhaus@rupprecht-consult.eu @H2020_CoEXist #H2020_CoEXist

21. Johan Olstam johan.olstam@vti.se #H2020CoEXist @H2020_CoEXist The sole responsibility for the content of this document lies with the authors. It does not necessarily reflect the opinion of the European Union. Neither the EASME nor the European Commission are responsible for any use that may be made of the information contained therein.