Abstract
In order to overcome the current driving fatigue method and system limitations, the paper proposes a driving fatigue detection method which is based on steering wheel angle features and support vector machines. This method evaluates the driving fatigue based on steering wheel angle, using AR model to analyze the time sequence and extract parameters from fixed-order model as the input vector, dividing the driving fatigue state into three degrees as output vector by the Stanford sleepiness scale, then using SVM as the classifier model, and optimizing SVM parameters with the method of CV on the basis of supporting vector machine theory. Finally, verified by specific examples, this method can effectively detect driving fatigue with a higher detection rate, so it has certain engineering application value.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sun W, Zhang W, Zhang X, Chen G (2009) Research progress of fatigue driving early warning system. Auto Electr Parts 1:4–8
Yan S, Wei J-Q, Wu Y-H (2005) Study of EEG features extraction for Doze car drivers.Chin. J Biomed Eng 24(1):110–113
Fang R-X (2010) Experimental research of effectiveness of pre-warning countermeasure of driving fatigue based on physiological. Beijing University of Technology
Kithil PW, Jones RD, John M (1998) Development of driver alertness detection system using overhead capacitive sensor array. SAE Technical Paper Series 982292
Guan Y, Song L (2010) Driver eye state detection based on support vector machine-decision tree. J Harbin Univ Sci Technol 15(6):5–8
Vapnik V (1998) Statistical learning theory. Wiley, New York, NY
Hsu CW, Lin CJ (2002) A comparison of methods for multi-class support vector machines. IEEE Trans Neural Network 13(2):415–425
Tan X (2009) The application of support vector machine in wavelet packet De-noising. Shanghai Jiao Tong University
Huang J (2011) The research of support vector machine algorithm and the application in meteorological data mining. China University of Mining and Technology
Tang B-P, Liu W-Y, Jiang Y (2010) Parameter optimized morlet wavelet de-noising method on cross validation method. J Chongqing Univ 33(1):1–5
Liu XY, Li P, Gao C (2011) Fast leave one out cross validation algorithm for extreme learning machine. J Shanghai Jiao Tong Univ 45(8):1140–1144
Chalder T, Berelowitz G, Pawlikowska T et al (1993) Development of a fatigue scale. J Psychosom Res 37(2):147–153
Yin A-D (2002) Research on the applications of fuzzy classification in the gears fault. HeFei University of Technology
Yang WK, Ji SB, Ren MW (2009) Face extraction based on enhance 2D PCA and its application to face recognition. J Image Graph 14(2):227–232
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, L., Yang, D., Ni, H., Yu, T. (2019). Driver Fatigue Detection Based on SVM and Steering Wheel Angle Characteristics. In: (SAE-China), S. (eds) Proceedings of the 19th Asia Pacific Automotive Engineering Conference & SAE-China Congress 2017: Selected Papers. SAE-China 2017. Lecture Notes in Electrical Engineering, vol 486. Springer, Singapore. https://doi.org/10.1007/978-981-10-8506-2_49
Download citation
DOI: https://doi.org/10.1007/978-981-10-8506-2_49
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8505-5
Online ISBN: 978-981-10-8506-2
eBook Packages: EngineeringEngineering (R0)