Highway vehicle accident reconstruction using cooperative collision warning based motor vehicle event data recorder

Chung-Ping Young, Bao Rong Chang, Ting Ying Wei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

There is often uncertainty or negligence in measurements and calculations by collecting relevant evidence and site information at the scene for the conventional vehicle accident analysis and reconstruction. Cooperative Collision Warning (CCW) mechanism exchanging static and dynamic vehicle information with neighboring vehicles through inter-vehicle wireless communications provides an active safety mechanism for vehicles on highways. Received information is not only used for calculating the relative safety distance between neighboring vehicles, but also preserved in a Motor Vehicle Event Data Recorder (MVEDR) for future accident reconstruction. A CCW-based MVEDR can easily rebuild the trajectory and interaction between the host and neighboring vehicles within communication range. This device is a supplementary tool for conventional accident reconstruction, and it saves time, labor and cost required for investigations, and eliminates uncertainty regarding accident analysis.

Original languageEnglish
Title of host publication2009 IEEE Intelligent Vehicles Symposium
Pages1131-1136
Number of pages6
DOIs
Publication statusPublished - 2009 Nov 20
Event2009 IEEE Intelligent Vehicles Symposium - Xi'an, China
Duration: 2009 Jun 32009 Jun 5

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings

Other

Other2009 IEEE Intelligent Vehicles Symposium
CountryChina
CityXi'an
Period09-06-0309-06-05

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Automotive Engineering
  • Computer Science Applications

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  • Cite this

    Young, C-P., Chang, B. R., & Wei, T. Y. (2009). Highway vehicle accident reconstruction using cooperative collision warning based motor vehicle event data recorder. In 2009 IEEE Intelligent Vehicles Symposium (pp. 1131-1136). [5164441] (IEEE Intelligent Vehicles Symposium, Proceedings). https://doi.org/10.1109/IVS.2009.5164441