TY  - JOUR
PY  - 2011//
TI  - Online driver distraction detection using long short-term memory
JO  - IEEE transactions on intelligent transportation systems
A1  - Wollmer, M.
A1  - Blaschke, C.
A1  - Schindl, T.
A1  - Schuller, B.
A1  - Farber, B.
A1  - Mayer, S.
A1  - Trefflich, B.
SP  - 574
EP  - 582
VL  - 12
IS  - 2
N2  - 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 />  
LA  - 
SN  - 1524-9050
UR  - http://dx.doi.org/10.1109/TITS.2011.2119483
ID  - ref1
ER  -