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.


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    Title :

    A probabilistic model of a set of driving decisions


    Contributors:
    Kurt, A. (author) / Ozguner, U. (author)


    Publication date :

    2011-10-01


    Size :

    567789 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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