Online Driver Distraction Detection Using Long Short-Term Memory

By Martin Wöllmer, C. Blaschke, T. Schindl, Björn Schuller, B. Färber, S. Mayer, B. Trefflich. 2011

169 citations recorded by the source corpus.

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).

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Human-Computer Interaction

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Citations in the corpus

Deep learning in neural networks: An overview (2014, methodology): “LSTM is also applicable to robot localization (Förster, Graves, & Schmidhuber, 2007), robot control (Mayer et al., 2008), online driver distraction detection (Wöllmer et al., 2011), and many other tasks.”
Online Human Action Detection using Joint Classification-Regression Recurrent Neural Networks (2016, methodology): “lying RNNs for the action detection and forecasting tasks. Motivated by the advantages of RNNs in sequence learning and some other online detection tasks (e.g. audio onset [23] and driver distraction [24] detection), we propose a Joint Classiﬁcation and Regression RNN to automatically localize the a”