# A taxonomy of strategic human interactions in traffic conflicts

arXiv: https://arxiv.org/abs/2109.13367 · HTML: https://mlsanity.com/paper/2109.13367/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 27 Sep 2021.

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

- [On a Formal Model of Safe and Scalable Self-driving Cars](https://mlsanity.com/paper/1708.06374/) (2017)
- [Hierarchical Game-Theoretic Planning for Autonomous Vehicles](https://mlsanity.com/paper/1810.05766/) (2018)
- [Urban Driving with Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/1811.08586/) (2018)
- [Agreeing to Cross: How Drivers and Pedestrians Communicate](https://mlsanity.com/paper/1702.03555/) (2017)
- [Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers](https://mlsanity.com/paper/2008.07303/) (2020)
- [Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts](https://mlsanity.com/paper/1810.00829/) (2018)
- [Decision Making for Autonomous Vehicles at Unsignalized Intersection in Presence of Malicious Vehicles](https://mlsanity.com/paper/1904.10158/) (2019)
- [Solution Concepts in Hierarchical Games under Bounded Rationality with Applications to Autonomous Driving](https://mlsanity.com/paper/2009.10033/) (2020)
- [Game Theoretic Analysis of Road User Safety Scenarios Involving Autonomous Vehicles](https://mlsanity.com/paper/1803.02393/) (2018)
- [Eye Contact Between Pedestrians and Drivers](https://mlsanity.com/paper/1904.04188/) (2019)

## Abstract (arXiv)

In order to enable autonomous vehicles (AV) to navigate busy traffic situations, in recent years there has been a focus on game-theoretic models for strategic behavior planning in AVs. However, a lack of common taxonomy impedes a broader understanding of the strategies the models generate as well as the development of safety specification to identity what strategies are safe for an AV to execute. Based on common patterns of interaction in traffic conflicts, we develop a taxonomy for strategic interactions along the dimensions of agents' initial response to right-of-way rules and subsequent response to other agents' behavior. Furthermore, we demonstrate a process of automatic mapping of strategies generated by a strategic planner to the categories in the taxonomy, and based on vehicle-vehicle and vehicle-pedestrian interaction simulation, we evaluate two popular solution concepts used in strategic planning in AVs, QLk and Subgame perfect ε-Nash Equilibrium, with respect to those categories.