Driver modeling using a continuous policy space: theory and traffic data validation

Abstract

In this article, we present a continuous-policy-space game theoretical method for modeling human driver interactions on highway traffic. The proposed method is based on Gaussian Processes and developed as a refinement of the hierarchical decision-making concept called “level- k reasoning” that conventionally assigns discrete levels of behaviors to agents. Conventional 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. To fill this gap in the literature, we expand the framework to a continuous domain that enables a continuous-policy-space, consisting of infinitely many driver policies. Through the approach detailed in this article, more accurate and realistic driver models can be obtained and employed for creating high-fidelity simulation platforms for the validation of autonomous vehicle control algorithms. We validate the proposed method on a traffic dataset and compare it with the conventional level- k approach to demonstrate its contributions and implications.