This paper introduces a dangerous-driving warning system that uses statistical modeling to predict driving risks. The major challenge of the research is how to discover the safe/dangerous driving patterns from a sparsely labeled training data set. This paper proposes a semisupervised learning method to utilize both the labeled and the unlabeled data, as well as their interdependence to build a proper danger-level function. In addition, the learned function adopts a continuous parametric form, which is more suitable in modeling the continuous safe/dangerous-driving state transitions in a practical dangerous-driving warning system. Our comprehensive experimental evaluations reveal that, in comparison with driving danger-level estimation using classification-based methods, such as the hidden Markov model (HMM) or the conditional random field algorithm, the proposed method requires less training time and achieved higher prediction accuracy.


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    Title :

    Driving Safety Monitoring Using Semisupervised Learning on Time Series Data


    Contributors:
    Jinjun Wang, (author) / Shenghuo Zhu, (author) / Yihong Gong, (author)


    Publication date :

    2010-09-01


    Size :

    1606255 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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