A probabilistic model of a set of driving decisions

This study proposes a probabilistic decision-making model for driving decisions. The decision-making process that is modeled stochastically is part of the Human Driver Model developed in an earlier study, in which perception, world-model and reflexive behavior are represented as separate modules. Finite-state machine design guidelines for decision-making models are provided to maximize state observability and resolution while maintaining a manageable size for state-machine. Two decision-making models useful for estimation and prediction of driver behavior are presented and one scenario-safety estimation application that uses the proposed decision-making model is given to illustrate the proposed methodology.