Online Driver Distraction Detection Using Long Short-Term Memory

doi:10.1109/TITS.2011.2119483

Online Driver Distraction Detection Using Long Short-Term Memory

2011-06-01

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DOI：10.1109/TITS.2011.2119483

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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).

Keywords:

Driver assistance systems

driver state estimation

long short-term memory (LSTM)

recurrent neural networks (RNNs)

Online Driver Distraction Detection Using Long Short-Term Memory

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