Lane-keeping assistance systems for vehicles may be more acceptable to users if the assistance was adaptive to the driver's state. To adapt systems in this way, a method for detection of driver distraction is needed. Thus, we propose a novel technique for online detection of driver's distraction, modeling the long-range temporal context of driving and head tracking data. We show that long short-term memory (LSTM) recurrent neural networks enable a reliable subject-independent detection of inattention with an accuracy of up to 96.6%. Thereby, our LSTM framework significantly outperforms conventional approaches such as support vector machines (SVMs).


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    Title :

    Online Driver Distraction Detection Using Long Short-Term Memory


    Contributors:
    Wollmer, M (author) / Blaschke, C (author) / Schindl, T (author) / Schuller, B (author) / Farber, B (author) / Mayer, S (author) / Trefflich, B (author)


    Publication date :

    2011-06-01


    Size :

    392803 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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