Modeling Human Driver Interactions Using an Infinite Policy Space Through Gaussian Processes

TL;DR

This paper introduces a continuous policy space modeling approach using Gaussian processes to better capture human driver interactions, surpassing traditional discrete level-k reasoning methods for more accurate predictions.

Contribution

It presents an infinite policy space framework with Gaussian processes, enhancing human driver behavior modeling over conventional discrete level-k approaches.

Findings

The proposed method outperforms traditional level-k models in accuracy.

Validation on real traffic data demonstrates improved driver behavior prediction.

The approach enables high-fidelity autonomous vehicle simulation environments.

Abstract

This paper proposes a method for modeling human driver interactions that relies on multi-output gaussian processes. The proposed method is developed as a refinement of the game theoretical hierarchical reasoning approach called "level-k reasoning" which conventionally assigns discrete levels of behaviors to agents. Although it is 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 extracts. 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 can be obtained, which can then be employed for creating high fidelity simulation platforms for the validation of autonomous vehicle control algorithms. The proposed method is validated on a real traffic dataset and compared with the conventional level-k approach to demonstrate its contributions and implications.