Drowsy behavior detection based on driving information

详细信息    查看全文

• 作者：M. S. Wang ; N. T. Jeong ; K. S. Kim…
• 关键词：Drowsy behavior ; Acceleration ; Steering angle ; Random forest ; Ensemble machine learning method ; Vehicle safety
• 刊名：International Journal of Automotive Technology
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：17
• 期：1
• 页码：165-173
• 全文大小：874 KB
• 参考文献：Albert, A. M. (2009). Random Forests. [Lecture Notes]. Statistical Foundations of Data Analysis. Temple University. Philadelphia. USA.
  Breiman, L. (2001). Random forests. Machine Learning 45, 1, 5–32.CrossRef MATH
  Eskandarian, A., Sayed, R., Delaigue, P., Mortazavi, A. and Blum, J. (2007). Advanced Driver Fatigue Research (No. FMCSA-RRR-07-001).CrossRef
  Fu, R. and Wang, H. (2014). Detection of driving fatigue by using noncontact emg and ecg signals measurement system. Int. J. Neural Systems 24, 3.CrossRef
  Horne, J. A. and Reyner, L. A. (1996). Counteracting driver sleepiness: Effects of napping, caffeine, and placebo. Psychophysiology, 33, 306–309.CrossRef
  Li, G. and Chung, W. Y. (2013). Detection of driver drowsiness using wavelet analysis of heart rate variability and a support vector machine classifier. Sensors 13, 12, 16494–16511.CrossRef MathSciNet
  Jeong, N. T. and Suh, M. W. (2013). Performance analysis of green car using virtual integrated development environment. 2nd Int. Conf. Mechanical, Automobile and Robotics Engineering (ICMAR), Dubai (UAE).
  Khushaba, R. N., Elliott, R., AlSukker, A., Al-Ani, A. and McKinley, S. (2010). Orthogonal locality sensitive fuzzy discriminant analysis in sleep-stage scoring. Pattern Recognition (ICPR), 2010 20th Int. Conf. IEEE, 165–168.CrossRef
  Krajewski, J., Sommer, D., Trutschel, U., Edwards, D. and Golz, M. (2009). Steering wheel behavior based estimation of fatigue. Proc. 5th Int. Driving Symp. Human Factors in Driver Assessment, Training and Vehicle Design, 118–124.
  McDonald, A. D., Schwarz, C., Lee, J. D. and Brown, T. L. (2012). Real-time detection of drowsiness related lanedepartures using steering wheel angle. Proc. Human Factors and Ergonomics Society Annual Meeting, 56, 2201–2205.CrossRef
  MacLean, A. W., Davies, D. R. and Thiele, K. (2003). The hazards and prevention of driving while sleepy. Sleep Medicine Reviews 7, 6, 507–521.CrossRef
  Niedermeyer, E. and da Silva, F. L. (2005). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins. Philadelphia. USA.
  NHTSA (2006). A Compilation of Motor Vehicle Crash Data from the Fatality Analysis Reporting System and the General Estimates System. National Highway Traffic Safety Administration. Traffic Safety Facts. NHTSA Final Report: DOT HS 810 818, US,Washington, DC.
  Pack, A. L., Pack, A. M., Rodgman, E., Cucchiara, A., Dinges, D. and Schwab, C. (1995). Characteristics of crashes attributed to the driver having fallen asleep. Accid. Anal. Prev., 27, 769–775.
  Park, C. H., Kwon, M., Jeong, N., Lee, S., Suh, M., Kim, H. and Hwang, S. (2014). Development of electric vehicle simulator for performance analysis. Universal J. Mechanical Engineering 2, 7, 231.239.
  Robertson, G., Caldwell, G., Hamill, J., Kamen, G. and Whittlesey, S. (2013). Research Methods in Biomechanics. 2nd Edn. Human Kinetics. Champaign. USA.
  Sayed, R. and Eskandarian, A. (2001). Unobtrusive drowsiness detection by neural network learning of driver steering. Proc. Institution of Mechanical Engineers, Part D: J. Automobile Engineering 215, 9, 969.975.CrossRef
  The Free Dictionary (2010). The Free Dictionary by Farlex. [Online] Available from: http:// encyclopedia2thefreedictionarycom/ Reaction+Time+Human [Accessed: 8th August 2014].
  Wierwille, W. W. and Ellsworth, L. A. (1994). Evaluation of driver drowsiness by trained raters. Accident Analysis and Prevention 26, 5, 571.CrossRef
  
• 作者单位：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. Graduate School of Mechanical Engineering, Sungkyunkwan University, Gyeonggi, 16419, Korea
  2. Department of Automobile Engineering, Osan University, Gyeonggi, 18119, Korea
  3. School of Mechanical Engineering, Sungkyunkwan University, Gyeonggi, 16419, Korea
  
• 刊物类别：Engineering
• 刊物主题：Automotive and Aerospace Engineering and Traffic
  
• 出版者：The Korean Society of Automotive Engineers
• ISSN：1976-3832

文摘

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