Non-Connected Vehicle Detection Using Connected Vehicles

Connected vehicle (CV) technologies are entering the realm of deployment. They have the potential to help drivers and vehicles make safe, reliable and informed decisions, and thereby to enhance network capacity and reduce congestion. However, during the transition to CV technologies, there will be mixed traffic streams of CVs (with vehicle-tovehicle communication capabilities) and non-CVs. To improve the efficiency and reliability of traffic operations under mixed CV environments, there is the need not only for observable CV location data, but also unobservable non-CV location/trajectory to realize efficient and reliable CV-based applications. This study proposes a hidden Markov model, which is a probabilistic inference approach, to identify non-CV locations/trajectories. This methodology will be integrated with a cooperative-situation awareness framework. The proposed model will be analyzed using real-world vehicle trajectory data to aid the situational awareness of CVs under low market penetration rates.


  • English


  • Status: Active
  • Contract Numbers:


  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Center for Connected and Automated Transportation

    University of Michigan, Ann Arbor
    Ann Arbor, MI  United States  48109
  • Performing Organizations:

    Purdue University, Lyles School of Civil Engineering

    550 Stadium Mall Drive
    West Lafayette, IN  United States  47907
  • Principal Investigators:

    Peeta, Srinivas

  • Start Date: 20170801
  • Expected Completion Date: 20180731
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01645394
  • Record Type: Research project
  • Source Agency: Center for Connected and Automated Transportation
  • Contract Numbers: 69A3551747105
  • Files: UTC, RiP
  • Created Date: Aug 31 2017 2:30PM