Driver Modeling Using Continuous Reasoning Levels: A Game Theoretical Approach

The focus of this paper is designing a game theoretical method using a continuous policy space for modeling human driver interactions on highway traffic. The proposed method is based on Gaussian Processes and developed as an enrichment to the hierarchical decision-making concept called "level-k reasoning". This concept conventionally assigns discrete levels of behaviors to agents. Although shown to be an effective modeling tool, the level-k reasoning approach may pose undesired constraints for predicting human decision making due to a limited number (usually 2 or 3) of driver policies it provides. The proposed approach is put forward to fill this gap in the literature by introducing a continuous domain framework that enables an infinite policy space. By using the approach presented in this paper, more accurate driver models are obtained, which can be employed for creating high-fidelity simulation platforms for the validation of autonomous vehicle control algorithms. The proposed method is validated on a traffic dataset and compared with the conventional level- k approach to demonstrate its contributions and implications.