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International Journal of Automotive Technology

, Volume 17, Issue 1, pp 165–173 | Cite as

Drowsy behavior detection based on driving information

  • M. S. Wang
  • N. T. Jeong
  • K. S. Kim
  • S. B. Choi
  • S. M. Yang
  • S. H. You
  • J. H. Lee
  • M. W. Suh
Article

Abstract

Drowsy behavior is more likely to occur in sleep-deprived drivers. Individuals’ drowsy behavior detection technology should be developed to prevent drowsiness related crashes. Driving information such as acceleration, steering angle and velocity, and physiological signals of drivers such as electroencephalogram (EEG), and eye tracking are adopted in present drowsy behavior detection technologies. However, it is difficult to measure physiological signal, and eye tracking requires complex experiment equipment. As a result, driving information is adopted for drowsy driving detection. In order to achieve this purpose, driving experiment is performed for obtaining driving information through driving simulator. Moreover, this paper investigates effects of using different input parameter combinations, which is consisted of lateral acceleration, longitudinal acceleration, and steering angles with different time window sizes (i.e. 4 s, 10 s, 20 s, 30 s, 60 s), on drowsy driving detection using random forest algorithm. 20 s-size datasets using parameter combination of accelerations in lateral and longitudinal directions, compared to the other combination cases of driving information such as steering angles combined with lateral and longitudinal acceleration, steering angles only, longitudinal acceleration only, and lateral acceleration only, is considered the most effective information for drivers’ drowsy behavior detection. Moreover, comparing to ANN algorithm, RF algorithm performs better on processing complex input data for drowsy behavior detection. The results, which reveal high accuracy 84.8 % on drowsy driving behavior detection, can be applied on condition of operating real vehicles.

Key Words

Drowsy behavior Acceleration Steering angle Random forest Ensemble machine learning method Vehicle safety 

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Copyright information

© The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • M. S. Wang
    • 1
  • N. T. Jeong
    • 1
  • K. S. Kim
    • 1
  • S. B. Choi
    • 1
  • S. M. Yang
    • 1
  • S. H. You
    • 1
  • J. H. Lee
    • 2
  • M. W. Suh
    • 3
  1. 1.Graduate School of Mechanical EngineeringSungkyunkwan UniversityGyeonggiKorea
  2. 2.Department of Automobile EngineeringOsan UniversityGyeonggiKorea
  3. 3.School of Mechanical EngineeringSungkyunkwan UniversityGyeonggiKorea

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