In this paper a novelty method to combine knowledge of traffic safety experts, in order to detect driving risk situations, is presented. A set of driving sessions were executed in a very realistic truck simulator where several magnitudes and visual information from the vehicle, driver and road were collected. Two kind of experiments were designed: controlled driving sessions (where several risky situations were induced), and natural driving sessions (where a natural driving behavior was expected). A group of traffic safety experts were consulted to evaluate the driving risk in each session. The information acquired from the traffic safety experts was used to develop a methodology to combine the information and to define a set of driving risk models. The developed system detected most of the induced risk situations besides of several non-induced risk situations. The methodology presented in this paper can be used to obtain a driving risk ground truth in order to compare and evaluate risk detection algorithms and to analyze the influence of vehicle, driver and road variables on the driving risk.


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    Title :

    Combining traffic safety knowledge for driving risk detection


    Contributors:
    Siordia, O. S. (author) / de Diego, I. M. (author) / Conde, C. (author) / Cabello, E. (author)


    Publication date :

    2011-10-01


    Size :

    1607325 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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