• DocumentCode
    3766580
  • Title

    Early car collision prediction in VANET

  • Author

    Qiong Wu;Lucas C. K. Hui;C. Y. Yeung;T. W. Chim

  • Author_Institution
    Department of Computer Science, The University of Hong Kong
  • fYear
    2015
  • Firstpage
    94
  • Lastpage
    99
  • Abstract
    Traffic safety is critical to our lives. In recent years, the increasing traffic accidents have brought considerable deaths. Reducing the rate of car accident has become one of the most important tasks in transportation field. VANET is a widely used platform contributing to possible solutions. In the past, there are protocols to generate or broadcast warning messages after vehicle collision. However, since most of them are made after car accidents have already occurred, they may help accident investigation but do not prevent the accident from taking place. In this paper, we proposed a method using support vector machine(SVM) [1] for early car accident detection in VANET. Once any dangerous situation is predicted, immediately the endangered driver gets a alert along with a suggestion to avoid danger.
  • Keywords
    "Vehicles","Accidents","Vehicular ad hoc networks","Support vector machines","Acceleration","Data models","Roads"
  • Publisher
    ieee
  • Conference_Titel
    Connected Vehicles and Expo (ICCVE), 2015 International Conference on
  • Electronic_ISBN
    2378-1297
  • Type

    conf

  • DOI
    10.1109/ICCVE.2015.55
  • Filename
    7447652