  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.
