@article{ref1,
title="Online driver distraction detection using long short-term memory",
journal="IEEE transactions on intelligent transportation systems",
year="2011",
author="Wollmer, M. and Blaschke, C. and Schindl, T. and Schuller, B. and Farber, B. and Mayer, S. and Trefflich, B.",
volume="12",
number="2",
pages="574-582",
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;<p /> ",
language="",
issn="1524-9050",
doi="10.1109/TITS.2011.2119483",
url="http://dx.doi.org/10.1109/TITS.2011.2119483"
}