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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    Titel :

    Online Driver Distraction Detection Using Long Short-Term Memory


    Beteiligte:
    Wollmer, M (Autor:in) / Blaschke, C (Autor:in) / Schindl, T (Autor:in) / Schuller, B (Autor:in) / Farber, B (Autor:in) / Mayer, S (Autor:in) / Trefflich, B (Autor:in)


    Erscheinungsdatum :

    2011-06-01


    Format / Umfang :

    392803 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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