2020/06/15 by Bhattacharyya, Raunak P., Brown, Kyle, Kyle Brown +5
Engineering · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #Computers and Society (cs.CY) #FOS: Computer and information sciences #FOS: Electrical engineering #Systems and Control (eess.SY) #Traffic Prediction and Management Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2006.08832
openalex publication_date 2020/06/15 · openalex created_date 2020/12/07 · openalex updated_date 2026/07/28
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.