This paper introduces a driving danger-level prediction system that uses multiple sensor inputs and statistical modeling to predict the driving risk. Three types of features were collected for the research, specifically the vehicle dynamic parameter, the driver's physiological data and the driver's behavior feature. To model the temporal patterns that lead to safe/dangerous driving state, several sequential supervised learning algorithms were evaluated in the paper, including hidden Markov model, conditional random field and reinforcement learning. Experimental results showed that using reinforcement learning based method with the vehicle dynamic parameters feature outperforms the rest algorithms, and adding the other two features could further improve the prediction accuracy. Based on the result, a live driving danger-level prediction prototype system was developed. Compared to many previous researches that focused on monitoring the driver's vigilance level to infer the possibility of potential driving risk, the authrs' live system is non-intrusive to the driver, and hence it is very desirable for driving danger prevention applications. Subjective on-line user study of their prototype system gave promising results.


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    Title :

    Real-time driving danger-level prediction


    Additional title:

    Echtzeitvorhersage von Gefahrensituationen bei Autofahren


    Contributors:
    Wang, Jinjun (author) / Xu, Wei (author) / Gong, Yihong (author)


    Publication date :

    2010


    Size :

    8 Seiten, 7 Bilder, 4 Tabellen, 26 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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