Driver behavior detection and classification using deep convolutional neural networks

By M. Shahverdy, Mahmood Fathy, R. Berangi, Mohammad Sabokrou. Expert systems with applications, 2020

304 citations recorded by the source corpus.

Abstract 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.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/5667152a809fc7b51c288132b5511b409613a45a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

  1. Iis P. Tussyadiah
  2. Florian J. Zach
  3. Daniel A. Guttentag
  4. Sita Mishra
  5. Orhan Bahadır Doğan
  6. S. Smith
  7. V. Kumar
  8. Sheetal Jain

In Computer Vision and Pattern Recognition

  1. João Guerreiro
  2. Sandra Maria Correia Loureiro
  3. Iis P. Tussyadiah

Citations in the corpus