Driver behavior detection and classification using deep convolutional neural networks

doi:10.1016/j.eswa.2020.113240

Driver behavior detection and classification using deep convolutional neural networks

2020-07-01

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DOI：10.1016/j.eswa.2020.113240

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Driver behavior monitoring system as Intelligent Transportation Systems (ITS) have been widely exploited to reduce the traffic accidents risk. Most previous methods for monitoring the driver behavior are rely on computer vision techniques. Such methods suffer from violation of privacy and the possibility of spoofing. This paper presents a novel yet efficient deep learning method for analyzing the driver behavior. We have used the driving signals, including acceleration, gravity, throttle, speed, and Revolutions Per Minute (RPM) to recognize five types of driving styles, including normal, aggressive, distracted, drowsy, and drunk driving. To take the advantages of successful deep neural networks on images, we learn a 2D Convolutional Neural Network (CNN) on images constructed from driving signals based on recurrence plot technique. Experimental results confirm that the proposed method can efficiently detect the driver behavior. (C) 2020 Elsevier Ltd. All rights reserved.

Keywords:

Driver behavior

Recurrence plot

Convolutional neural networks

Deep learning

Driver behavior detection and classification using deep convolutional neural networks

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