Act to Reason: A Dynamic Game Theoretical Model of Driving

TL;DR

This paper introduces a dynamic game theoretical driver model that incorporates human reasoning levels as actions, enabling more realistic and adaptive driving behavior in traffic scenarios.

Contribution

It proposes a novel dynamic approach where reasoning levels are actions, improving realism over fixed-level models in driver interaction simulations.

Findings

More realistic driving behaviors in highway merging scenarios

Adaptive reasoning levels enhance model flexibility

Outperforms fixed-level models in dynamic traffic situations

Abstract

The focus of this paper is to propose a driver model that incorporates human reasoning levels as actions during interactions with other drivers. Different from earlier work using game theoretical human reasoning levels, we propose a dynamic approach, where the actions are the levels themselves, instead of conventional driving actions such as accelerating or braking. This results in a dynamic behavior, where the agent adapts to its environment by exploiting different behavior models as available moves to choose from, depending on the requirements of the traffic situation. The bounded rationality assumption is preserved since the selectable strategies are designed by adhering to the fact that humans are cognitively limited in their understanding and decision making. Using a highway merging scenario, it is demonstrated that the proposed dynamic approach produces more realistic outcomes compared to the conventional method that employs fixed human reasoning levels.