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

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Document pages: 19 pages

Abstract: We present a review and taxonomy of 200 models from the literature on driverbehavior modeling. We begin by introducing a mathematical framework fordescribing the dynamics of interactive multi-agent traffic. Based on thepartially observable stochastic game, this framework provides a basis fordiscussing different driver modeling techniques. Our taxonomy is constructedaround the core modeling tasks of state estimation, intention estimation, traitestimation, and motion prediction, and also discusses the auxiliary tasks ofrisk estimation, anomaly detection, behavior imitation and microscopic trafficsimulation. Existing driver models are categorized based on the specific tasksthey address and key attributes of their approach.

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