Modeling and Prediction of Human Driver Behavior: A Survey

Brown, Kyle, Driggs-Campbell, Katherine, Kochenderfer, Mykel J. Artificial Intelligence 

We present a review and taxonomy of 200 models from the literature on driver behavior modeling. We begin by introducing a mathematical formulation based on the partially observable stochastic game, which serves as a common framework for comparing and contrasting different driver models. 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.

  Country: North America > United States (0.67)
  Genre: Overview (0.87)

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