A Utility Maximization Model of Pedestrian and Driver Interactions
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Abstract (arXiv)
Many models account for the traffic flow of road users but few take the details of local interactions into consideration and how they could deteriorate into safety-critical situations. Building on the concept of sensorimotor control, we develop a modeling framework applying the principles of utility maximization, motor primitives, and intermittent action decisions to account for the details of interactive behaviors among road users. The framework connects these principles to the decision theory and is applied to determine whether such an approach can reproduce the following phenomena: When two pedestrians travel on crossing paths, (a) their interaction is sensitive to initial asymmetries, and (b) based on which, they rapidly resolve collision conflict by adapting their behaviors. When a pedestrian crosses the road while facing an approaching car, (c) either road user yields to the other to resolve their conflict, akin to the pedestrian interaction, and (d) the outcome reveals a specific situational kinematics, associated with the nature of vehicle acceleration. We show that these phenomena emerge naturally from our modeling framework when the model can evolve its parameters as a consequence of the situations. We believe that the modeling framework and phenomenon-centered analysis offer promising tools to understand road user interactions. We conclude with a discussion on how the model can be instrumental in studying the safety-critical situations when including other variables in road-user interactions.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 56 references that are in the map. First posted on arXiv 21 Oct 2021.
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10 of 56 references.
References · 10
- 2018Convolutional Social Pooling for Vehicle Trajectory Predictioncited by 221
- 2021TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviorscited by 192
- 2018Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practicecited by 74
- 2018Learning from Demonstration in the Wildcited by 25
- 2020Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behaviorcited by 23
- 2020BARK: Open Behavior Benchmarking in Multi-Agent Environmentscited by 21
- 2020Online Parameter Estimation for Human Driver Behavior Predictioncited by 6
- 2021A human factors approach to validating driver models for interaction-aware automated vehiclescited by 5
- 2017Sustained sensorimotor control as intermittent decisions about prediction errors: Computational framework and application to ground vehicle steeringcited by 3
- 2021Comparing merging behaviors observed in naturalistic data with behaviors generated by a machine learned modelcited by 3
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