Distinguish method of fatigue state based on driving behavior wavelet analysis - 文章详情页

作者：

Hu, Dun-Ii (Hu, Dun-Ii.) | Zhao, Xiao-Hua (Zhao, Xiao-Hua.) | Mu, Zhi-Chun (Mu, Zhi-Chun.) | Sun, De-Hui (Sun, De-Hui.) | Liu, Kang (Liu, Kang.)

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EI Scopus

摘要：

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.　©　2013　TCCT,　CAA.

关键词：

Behavioral research Fatigue of materials Wavelet analysis Wavelet transforms

作者机构：

[ 1 ] [Hu, Dun-Ii]School of Information Engineering, University of Science and Technology, Beijing 100083, China
[ 2 ] [Hu, Dun-Ii]Beijing Key Laboratory of Fieldbus Technology and Automation, North China University of Technology, Beijing 100144, China
[ 3 ] [Zhao, Xiao-Hua]Transportation Research Center, Beijing University of Technology, Beijing 100022, China
[ 4 ] [Mu, Zhi-Chun]School of Information Engineering, University of Science and Technology, Beijing 100083, China
[ 5 ] [Sun, De-Hui]School of Information Engineering, University of Science and Technology, Beijing 100083, China
[ 6 ] [Liu, Kang]Beijing Key Laboratory of Fieldbus Technology and Automation, North China University of Technology, Beijing 100144, China

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