Modeling Human Driver Interactions Using an Infinite Policy Space Through Gaussian Processes
This work addresses the problem of creating more accurate driver models for autonomous vehicle simulation, though it is incremental as it refines an existing game-theoretical method.
The paper tackles the limitation of discrete policy spaces in modeling human driver interactions by introducing a continuous framework using multi-output Gaussian processes, which is validated on real traffic data and shows improved accuracy over the conventional level-k reasoning approach.
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.