Autonomous Vehicle Source Enumeration Exploiting Non-Cooperative UAV in Software Defined Internet of Vehicles

The traffic congestion and accidents can be relieved by deploying the software defined internet of vehicles (SDN-IoV). However, the traffic of pedestrians and vehicles is particularly heavy near commercial streets and campuses. In particular scenarios, the SDN-IoV may not ensure the quality of service (QoS) for pedestrians and vehicles. In this paper, the authors construct a novel system architecture consisting of multiple non-cooperative unmanned aerial vehicles (UAVs) and a SDN-IoV. The non-cooperative UAV is equipped with an antenna array to receive the signals from the vehicles and pedestrians of SDN-IoV. In order to locate the positions of vehicles and pedestrians, two source enumeration methods are proposed in a complex SDN-IoV environment with color noise. The projection matrix of the low dimensional signal subspace is constructed by the proposed criterion based on signal subspace projection (SSP). The sequence of the projected difference values of the local covariance matrix is applied to estimate the number of vehicles and pedestrians. The eigenvalues can be grouped to construct different subspaces by the proposed eigen-subspace projection (ESP). By projecting a new covariance matrix into the eigen-subspaces, the variance of values represents the projection difference can be exploited to estimate the number of vehicles and pedestrians. Simulation results and real system test verify the validity of the two proposed methods by comparing them with the state-of-the-art methods. Both of the methods have excellent estimation performance especially in color noise.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2020.3018377
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2021, IEEE.
• Authors:
  • Wan, Liangtian
  • Sun, Lu
  • Liu, Kaihui
  • Wang, Xianpeng
  • Lin, Qinyong
  • Zhu, Tiantian
• Publication Date: 2021-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 3603-3615
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 22
  • Issue Number: 6
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Drones; Pedestrians; Traffic congestion; Traffic safety; Vehicle to infrastructure communications; Vehicle to vehicle communications
• Subject Areas: Highways; Operations and Traffic Management; Pedestrians and Bicyclists; Vehicles and Equipment;

Filing Info

• Accession Number: 01788758
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 18 2021 12:12PM