• DocumentCode
    529535
  • Title

    Collision risk assessment for pedestrians´ safety : Neural network with interacting multiple model apporach

  • Author

    Park, Seongkeun ; Choi, Baehoon ; Baehoon Choi ; Kim, Euntai

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Comput. Intell. Lab., Seoul, South Korea
  • fYear
    2010
  • fDate
    18-21 Aug. 2010
  • Firstpage
    2897
  • Lastpage
    2900
  • Abstract
    In this paper, we propose a alarm system for pedestrian protection. We usually do not know that pedestrians may or may not be in dangerous situation, and to know whether pedestrians are in dangerous situation or not. In this paper, we construct collision probability system between vehicle and pedestrian. By using monte carlo simulation, we calculate the collision probability, and it is hard to know collision probability of all area, we recover collision probability of all area using neural networks. And, the collision probabilities are different according to tendency of pedestrian movement, we understand the tendency of pedestrian movement using interacting multiple model tracking method. Computer simulation will be show the validity of our proposed method.
  • Keywords
    Monte Carlo methods; alarm systems; collision avoidance; neural nets; risk management; road safety; traffic engineering computing; alarm system; collision probability system; collision risk assessment; interacting multiple model tracking method; monte carlo simulation; neural network; pedestrian movement; pedestrian protection; pedestrian safety; Artificial neural networks; Computational modeling; Driver circuits; Legged locomotion; Monte Carlo methods; Probability; Safety; Collision probability; Intelligent vehicle; Pedestrian protection system;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    SICE Annual Conference 2010, Proceedings of
  • Conference_Location
    Taipei
  • Print_ISBN
    978-1-4244-7642-8
  • Type

    conf

  • Filename
    5602858