DocumentCode :
2538165
Title :
Road characteristic identification based on wavelet neural network
Author :
Junhui, Lu ; Rongzheng, Zhou ; Jianjun, Ding ; Shijing, Wu
Author_Institution :
Phys. & Inf. Eng. Inst., Jianghan Univ., Wuhan, China
fYear :
2009
fDate :
3-5 June 2009
Firstpage :
1241
Lastpage :
1244
Abstract :
This paper presents a road characteristic identification method derived from wheel vibration. Firstly, analyses the friction principle between tires and road, road characteristic restrict road adhesion coefficient; Secondly, the wheel vibration model shows that wheel vibration mappings road characteristic; Thirdly, wheel vibration signal is decomposed by wavelet transform, using FFT get the high frequency spectrum vectors of wheel vibration; Finally, built and trained the RBF neural network classifier with the frequency spectrum vectors. For fine blacktop and mattess, the high frequency spectrum of wheel vibration displays obvious difference, the road type identification accuracy reaches 100%.
Keywords :
adhesion; fast Fourier transforms; learning (artificial intelligence); radial basis function networks; road safety; road vehicles; signal classification; traffic engineering computing; vectors; vibrations; wavelet transforms; wheels; FFT; RBF neural network training; fast Fourier transform; frequency spectrum vector; friction principle; road adhesion; road characteristic identification; vehicle safety; wavelet transform; wheel vibration signal classification; Adhesives; Frequency; Friction; Neural networks; Roads; Signal analysis; Signal mapping; Tires; Wavelet analysis; Wheels;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Vehicles Symposium, 2009 IEEE
Conference_Location :
Xi´an
ISSN :
1931-0587
Print_ISBN :
978-1-4244-3503-6
Electronic_ISBN :
1931-0587
Type :
conf
DOI :
10.1109/IVS.2009.5164460
Filename :
5164460
Link To Document :
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