A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior
Abstract
An open problem in autonomous driving research is modeling human driving behavior, which is needed for the planning component of the autonomy stack, safety validation through traffic simulation, and causal inference for generating explanations for autonomous driving. Modeling human driving behavior is challenging because it is stochastic, high-dimensional, and involves interaction between multiple agents. This problem has been studied in various communities with a vast body of literature. Existing reviews have generally focused on one aspect: motion prediction. In this article, we present a unification of the literature that covers intent estimation, trait estimation, and motion prediction. This unification is enabled by modeling multi-agent driving as a partially observable stochastic game, which allows us to cast driver modeling tasks as inference problems. We classify driver models into a taxonomy based on the specific tasks they address and the key attributes of their approach. Finally, we identify open research opportunities in the field of driver modeling.
Keywords
Cite
@article{arxiv.2006.08832,
title = {A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior},
author = {Raunak P. Bhattacharyya and Kyle Brown and Juanran Wang and Katherine Driggs-Campbell and Mykel J. Kochenderfer},
journal= {arXiv preprint arXiv:2006.08832},
year = {2026}
}
Comments
This revision has been accepted for publication in the Proceedings of the IEEE. The final version is available on IEEE Xplore