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Support Vector Machine Classification of Drunk Driving Behaviour


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1

College of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China

2

State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410012, China





*

Author to whom correspondence should be addressed.



Academic Editors: Amy O’Donnell, Eileen Kaner and Peter Anderson

Abstract Alcohol is the root cause of numerous traffic accidents due to its pharmacological action on the human central nervous system. This study conducted a detection process to distinguish drunk driving from normal driving under simulated driving conditions. The classification was performed by a support vector machine SVM classifier trained to distinguish between these two classes by integrating both driving performance and physiological measurements. In addition, principal component analysis was conducted to rank the weights of the features. The standard deviation of R–R intervals SDNN, the root mean square value of the difference of the adjacent R–R interval series RMSSD, low frequency LF, high frequency HF, the ratio of the low and high frequencies LF-HF, and average blink duration were the highest weighted features in the study. The results show that SVM classification can successfully distinguish drunk driving from normal driving with an accuracy of 70%. The driving performance data and the physiological measurements reported by this paper combined with air-alcohol concentration could be integrated using the support vector regression classification method to establish a better early warning model, thereby improving vehicle safety. View Full-Text

Keywords: drunk driving; support vector machine; principal component analysis; driving performance; physiological measurement drunk driving; support vector machine; principal component analysis; driving performance; physiological measurement





Autor: Huiqin Chen 1,2,* and Lei Chen 1

Fuente: http://mdpi.com/



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