• 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