Modeling and Prediction of Human Driver Behavior: A Survey
release_l4gnuo3edbeurnu6suwrif7dni
by
Kyle Brown and Katherine Driggs-Campbell and Mykel J. Kochenderfer
2020
Abstract
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.
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2006.08832v1
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