Drowsy behavior detection based on driving information
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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 WordsDrowsy behavior Acceleration Steering angle Random forest Ensemble machine learning method Vehicle safety
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- Albert, A. M. (2009). Random Forests. [Lecture Notes]. Statistical Foundations of Data Analysis. Temple University. Philadelphia. USA.Google Scholar
- 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).Google Scholar
- 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.Google Scholar
- Niedermeyer, E. and da Silva, F. L. (2005). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins. Philadelphia. USA.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- Robertson, G., Caldwell, G., Hamill, J., Kamen, G. and Whittlesey, S. (2013). Research Methods in Biomechanics. 2nd Edn. Human Kinetics. Champaign. USA.Google Scholar
- The Free Dictionary (2010). The Free Dictionary by Farlex. [Online] Available from: http:// encyclopedia2thefreedictionarycom/ Reaction+Time+Human [Accessed: 8th August 2014].Google Scholar