A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior

• URL: http://arxiv.org/abs/2006.08832v3
• Date: Sun, 29 Nov 2020 03:40:24 GMT
• Title: A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior
• Authors: Kyle Brown and Katherine Driggs-Campbell and Mykel J. Kochenderfer
• Abstract summary: We present a review and taxonomy of 200 models from the literature on driver behavior modeling. We begin by introducing a mathematical framework for describing the dynamics of interactive multi-agent traffic. Our taxonomy is constructed around the core modeling tasks of state estimation, intention estimation, trait estimation, and motion prediction.
• Score: 36.80532606715206
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a review and taxonomy of 200 models from the literature on driver behavior modeling. We begin by introducing a mathematical framework for describing the dynamics of interactive multi-agent traffic. Based on the partially observable stochastic game, this framework provides a basis for discussing different driver modeling techniques. Our taxonomy is constructed around the core modeling tasks of state estimation, intention estimation, trait estimation, and motion prediction, and also discusses the auxiliary tasks of risk estimation, anomaly detection, behavior imitation and microscopic traffic simulation. Existing driver models are categorized based on the specific tasks they address and key attributes of their approach.

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