In this investigation, driver behavior signals are modeled using Hidden Markov Models (HMM) in two different and complementary approaches. The first approach considers isolated maneuver recognition with model concatenation to construct a generic route (bottom-to-top), whereas the second approach models the entire route as a ‘phrase’ and refines the HMM to discover maneuvers and parses the route using finer discovered maneuvers (top-to-bottom). By applying these two approaches, a hierarchical framework to model driver behavior signals is proposed. It is believed that using the proposed approach, driver identification and distraction detection problems can be addressed in a more systematic and mathematically sound manner. We believe that this framework and the initial results will encourage more investigations into driver behavior signal analysis and related safety systems employing a partitioned sub-module strategy.


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    Title :

    Driver behavior analysis and route recognition by Hidden Markov Models


    Contributors:


    Publication date :

    2008-09-01


    Size :

    429050 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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