Road Characteristic Identification based on Wavelet Neural Network

被引：0
|
作者：
Lu Junhui [1 ,2 ]
Zhou Rongzheng [2 ]
Ding Jianjun [2 ]
Wu Shijing [1 ]
机构：
[1] Wuhan Univ, Coll Power & Mech Engn, Wuhan 430072, Peoples R China
[2] Jianghan Univ, Phys & Informat Engn Inst, Wuhan 430056, Peoples R China
来源：
2009 IEEE INTELLIGENT VEHICLES SYMPOSIUM, VOLS 1 AND 2 | 2009年
关键词：
D O I：
10.1109/IVS.2009.5164460
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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%.
引用
收藏
页码：1241 / 1244
页数：4
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