# A Utility Maximization Model of Pedestrian and Driver Interactions

arXiv: https://arxiv.org/abs/2110.11015 · HTML: https://mlsanity.com/paper/2110.11015/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 56 references that are in the map.
- First posted on arXiv 21 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors](https://mlsanity.com/paper/2101.06557/) (2021)
- [Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice](https://mlsanity.com/paper/1805.11773/) (2018)
- [Learning from Demonstration in the Wild](https://mlsanity.com/paper/1811.03516/) (2018)
- [Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior](https://mlsanity.com/paper/2003.11959/) (2020)
- [BARK: Open Behavior Benchmarking in Multi-Agent Environments](https://mlsanity.com/paper/2003.02604/) (2020)
- [Online Parameter Estimation for Human Driver Behavior Prediction](https://mlsanity.com/paper/2005.02597/) (2020)
- [A human factors approach to validating driver models for interaction-aware automated vehicles](https://mlsanity.com/paper/2109.13077/) (2021)
- [Sustained sensorimotor control as intermittent decisions about prediction errors: Computational framework and application to ground vehicle steering](https://mlsanity.com/paper/1703.03030/) (2017)
- [Comparing merging behaviors observed in naturalistic data with behaviors generated by a machine learned model](https://mlsanity.com/paper/2104.10496/) (2021)

## 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.