• DocumentCode
    2188796
  • Title

    Adaptive neural-fuzzy inference system based method to modeling of vehicle crash

  • Author

    Lin Zhao ; Pawlus, Witold ; Karimi, Hamid Reza ; Robbersmyr, K.G.

  • Author_Institution
    Dept. of Eng., Univ. of Agder, Grimstad, Norway
  • fYear
    2013
  • fDate
    Feb. 27 2013-March 1 2013
  • Firstpage
    370
  • Lastpage
    375
  • Abstract
    Various areas of research need to be considered in order to establish a mathematical model of a vehicle crash. To enhance the modeling process, a novel ANFIS-based approach to reconstruct behavior of impacting vehicles is presented in this paper. Kinematics of center of gravity (COG) a vehicle involved in an oblique barrier collision is reproduced by application of a five-layered ANFIS structure. Then, the same ANFIS system is used to simulate a different collision type than the one which was used in the training stage. The points of interests are selected to be the locations of accelerometers mounting. The accuracy of the proposed method is evaluated by the comparative analysis with the reference measurements from full-scale vehicle collisions.
  • Keywords
    fuzzy reasoning; traffic engineering computing; vehicles; COG; accelerometers mounting; adaptive neural-fuzzy inference system based method; center of gravity; full-scale vehicle collisions; mathematical model; oblique barrier collision; reference measurements; structure; vehicle crash; Acceleration; Accelerometers; Kinematics; Training; Training data; Vehicle crash testing; Vehicles; ANFIS-based modeling; artificial intelligence methods; kinematics reconstruction; vehicle crash simulation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechatronics (ICM), 2013 IEEE International Conference on
  • Conference_Location
    Vicenza
  • Print_ISBN
    978-1-4673-1386-5
  • Electronic_ISBN
    978-1-4673-1387-2
  • Type

    conf

  • DOI
    10.1109/ICMECH.2013.6518565
  • Filename
    6518565