An Architecture for Predicting Two-dimensional Traffic Collision Considering Shape

In this decade, the improvement of vehicular network gave impetus to vehicle collision warning systems (CWS), which serve to curb the injured and fatalities in crashes. Many position-based CWS and corresponding collision avoidance algorithms were developed but in the scenarios of two-dimension collision and multiple entities collision, the shape of all participants is usually ignored. In this paper, the shape of all manner of participants are subdivided to lots of squares based on their profile and filled in gridded road (the authors call this process gridding) and exploited to detect collision. In the simulated intersection collision scenario, the proposed CWS, which the authors integrate with the gridding method, shows more accuracy in spacing between participants (affected by the size of cells in gridding road), and the authors find that the overlapped cells – which depict the coincidence of the predicted trajectories of participants – show close relation between collision probability evaluation in simulation result. The results indicate that the gridding method can serve as another metric of collision prediction and is validated to measure and predict collision more precisely.

Language

  • English

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  • Accession Number: 01607684
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jul 5 2016 2:43PM