Driver behavior detection and classification using deep convolutional neural networks

• Published in: Expert systems with applications Vol. 149; p. 113240
• Main Authors: Shahverdy, Mohammad, Fathy, Mahmood, Berangi, Reza, Sabokrou, Mohammad
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.07.2020; Elsevier BV
• Subjects: Acceleration; Artificial neural networks; Computer vision; Convolutional neural networks; Deep learning; Driver behavior; Driving; Drunkenness; Intelligent transportation systems; Machine learning; Monitoring; Neural networks; Recurrence plot; Spoofing; Throttles; Traffic accidents; Transportation networks; Deep learning; Recurrence plot; Convolutional neural networks; Driver behavior
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2020.113240

•Monitoring the driver behavior is used for decreasing the risk of traffic accidents.•The driver behavior can be deduced from vehicle characteristics during driving.•Deep learning methods can use for analyzing driver behavior. 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.