A taxonomy of strategic human interactions in traffic conflicts
This work addresses a foundational gap for autonomous vehicle developers by providing a structured framework to analyze and ensure safety in strategic planning, though it is incremental as it builds on existing game-theoretic models.
The paper tackles the lack of a common taxonomy for strategic interactions in autonomous vehicles by developing a taxonomy based on traffic conflict patterns and demonstrates its application through automatic mapping and evaluation of solution concepts like QLk and Subgame perfect ε-Nash Equilibrium in simulations.
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.