Cooperative Positioning and Warning Systems via V2I Communications for Intelligent Vehicles

In future intelligent vehicle-infrastructure cooperation frameworks, accurate self-positioning is an important prerequisite for better driving environment evaluation (e.g., traffic safety and traffic efficiency). The authors herein describe a cooperative positioning and warning system (CPWS) for intelligent vehicles. In this system, the authors discuss the task allocation of CP-warning in time sequence and related packet frame format. Especially, in the cooperative positioning stage, an angle-domain parameter, i.e., two-dimensional angle-of-departure (AOD), is utilized. Under the cooperation of road-side units (RSUs) by vehicle to infrastructure (V2I) links, multiple AODs information are estimated and then transformed into vehicle's position. Given that the requirements for accuracy and computational efficiency, a new algorithm based on truncated signal subspace is proposed for AOD estimation, which can avoid the eigenvalue decomposition and the exhausted searching scheme such as MUSIC algorithm; and also an distance-based weighting strategy is proposed for fusing two independent position estimations, which can decrease the global positioning error. Numerical evaluations indicate: 1) the angle-domain parameter is feasible to be utilized for achieve sub-lane level localization; 2) the proposed angle estimation algorithm can be an effective choice if considering the accuracy and computational complexity; 3) Two RSUs cooperation have an obvious promoted performance over the individual positioning.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ABC40 Standing Committee on Transportation Asset Management.
  • Corporate Authors:

    Transportation Research Board

  • Authors:
    • Dong, Zhi
    • Yao, Bobin
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 6p

Subject/Index Terms

Filing Info

  • Accession Number: 01697864
  • Record Type: Publication
  • Report/Paper Numbers: 19-05272
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:40AM