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Online Driver Distraction Detection Using Long Short-Term Memory

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7 Author(s)
Wollmer, M. ; Inst. of Human-Machine-Commun., Tech. Univ. Munchen, München, Germany ; Blaschke, C. ; Schindl, T. ; Schuller, B.
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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).

Published in:

Intelligent Transportation Systems, IEEE Transactions on  (Volume:12 ,  Issue: 2 )