Citation

Wollmer M, Blaschke C, Schindl T, Schuller B, Farber B, Mayer S, Trefflich B. IEEE Trans. Intel. Transp. Syst. 2011; 12(2): 574-582.

Copyright

(Copyright © 2011, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2011.2119483

PMID

unavailable

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).


Keywords: Driver distraction;