Online Driver Distraction Detection Using Long Short-Term Memory

Authors:
M. Wollmer;C. Blaschke;T. Schindl;B. Schuller;B. Farber;S. Mayer;B. Trefflich
Affiliations:
Inst. of Human-Machine-Commun., Tech. Univ. Munchen, München, Germany;-;-;-;-;-;-
Venue:
IEEE Transactions on Intelligent Transportation Systems
Year:
2011

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Abstract

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).