DocumentCode :
1715137
Title :
Distinguish method of fatigue state based on driving behavior wavelet analysis
Author :
Dun-Ii Hu ; Xiao-hua Zhao ; Zhi-Chun Mu ; De-hui Sun ; Kang Liu
Author_Institution :
Sch. of Inf. Eng., Univ. of Sci. & Technol., Beijing, China
fYear :
2013
Firstpage :
3590
Lastpage :
3596
Abstract :
Being a direct reflection of the drivers´ states, the driving behavior research is getting widely attention recently. This paper presents a new identification method of fatigue driving state, which is obtained from driving behavior data analysis both about normal driving state and the fatigues ones. PERCLOS80 is utilized as reference to distinguish two different states. During identification process, the driving behavior data is dealt with wavelet transform. Then modulus maxima values and Lipschitz exponents which reflected smooth level of data signal are performed as index to identify driving states: normal or fatigue. Among various experimental driving behavior data, the error to driving center line is chosen as information source here, and the result shows remarkable identified effect.
Keywords :
behavioural sciences computing; data analysis; occupational stress; signal processing; wavelet transforms; Lipschitz exponents; PERCLOS80; driving behavior data analysis; driving behavior wavelet analysis; fatigue driving state identification method; fatigue state; information source; modulus maxima values; normal driving state; smooth data signal level; wavelet transform; Fatigue; Indexes; Time-frequency analysis; Vehicles; Wavelet analysis; Wavelet transforms; PERCLOS80; driving behavior; fatigue driving; wavelet transform;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2013 32nd Chinese
Conference_Location :
Xi´an
Type :
conf
Filename :
6640044
Link To Document :
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