This paper presents an application of diagnosis methods to the driver-vehicle-environment system. The study aims at characterizing the driving environment on the basis of data collected on the vehicle. 14 candidates drove on a driving simulator through 4 different driving situations: driving on a motorway with a dense traffic, driving on an A road with a dense traffic, driving on a motorway with a light traffic and driving on an A road with a light traffic. Multiple correspondence analysis (MCA) was used because it could provide a diagnosis without requiring a model of the studied system. Because MCA doesn't allow automated data classification, this first analysis is followed by a discriminant analysis, which provides 97% of well diagnosed driving situations. Finally, prospects that could enhance the diagnosis reliability are exposed.


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    Title :

    Driver's environment identification using automatic classification methods. Active safety application


    Contributors:
    Sonnerat, D. (author) / Tricot, N. (author) / Popieul, J. (author)

    Published in:

    Publication date :

    2002-01-01


    Size :

    383648 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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