A VANET-based Real-time Rear-End Collision Warning Algorithm

نویسندگان

  • Hexin Lv
  • Binbin Zhou
  • Huafeng Chen
  • Tiaojuan Ren
  • Yourong Chen
چکیده

Rear-end traffic collision has been a crucial problem due to numerous injury even death and corresponding economic and social damage. In order to eliminate these threats, a large number of researchers have paid effort on this area, with timeconsuming artificial intelligence-inspired methods or mathematical method in strict assumptions. In this paper, we propose a VANET-based real-time rear-end collision warning algorithm (VERCWA), to carry on real-time traffic risk assessment and inform drivers collision warning message in appropriate time. Traffic risk is evaluated with the consideration of space headway of the preceding vehicle and following vehicle, current speed of the relevant vehicles, drivers’ behavior characteristics (i.e. perception-reaction time) in a real-time way. Experiments conducted show that our VERCWA can obtain better performance, compared with HONDA algorithm with the utilization of a public dataset in California.

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تاریخ انتشار 2016