In this paper, we design distributed multi-modal localization approaches for Connected and Automated vehicles. We utilize information diffusion on graphs formed by moving vehicles, based on Adapt-then-Combine strategies coupled with the Least-Mean-Squares and the Conjugate Gradient algorithms. We treat the vehicular network as an undirected graph, where vehicles communicate with each other by means of Vehicle-to-Vehicle communication protocols. Connected vehicles perform cooperative fusion of different measurement modalities, including location and range measurements, in order to estimate both their positions and the positions of all other networked vehicles, by interacting only with their local neighborhood. The trajectories of vehicles were generated either by a well-known kinematic model, or by using the CARLA autonomous driving simulator. The proposed distributed and diffusion localization schemes significantly reduced the GPS error and do not only converged to the global solution, but they even outperformed it. Extensive simulation studies highlight the benefits of the various methods, which in turn outperform other state of the art approaches. The impact of the network connections and the network latency are also investigated.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Graph Laplacian Diffusion Localization of Connected and Automated Vehicles


    Contributors:


    Publication date :

    2022-08-01


    Size :

    5101481 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Graph Laplacian Extended Kalman Filter for Connected and Automated Vehicles Localization

    Nikos Piperigkos / Aris S. Lalos / Kostas Berberidis | BASE | 2021

    Free access

    Graph based Cooperative Localization for Connected and Semi-Autonomous Vehicles

    Nikos Piperigkos / Aris S. Lalos / Kostas Berberidis | BASE | 2020

    Free access