Empowering each vehicle with 4D situational awareness, i.e., accurate knowledge of neighboring vehicles' 3D locations over time in a cooperative manner, is fundamental for improving autonomous driving performance in diverse traffic conditions. For this task, identification, localization and tracking of road users is critical for enhancing safety, motion planning and energy consumption of automated vehicles. Advanced perception sensors as well as communication abilities, enable the close collaboration of moving vehicles, and significantly increase the positioning accuracy via multi-modal sensor fusion. The challenge here is to actually match the extracted measurements from perception sensors with the correct vehicle ID, through data association. In this paper, two novel and distributed Cooperative Localization algorithms are formulated, based on linear least-squares and the celebrated Kalman Filter. They both aim to improve ego vehicle's 4D situational awareness, so as to be fully location aware of its surrounding and not just its own position. For that purpose, ego vehicle forms a star like topology with its neighbors, and fuses four types of multi-modal inter-vehicular measurements via the linear Graph Laplacian operator and geometry capturing differential coordinates. Moreover, a data association strategy has been integrated to the algorithms as part of the identification process, which is shown to be much more beneficial than traditional Hungarian algorithm. An extensive and realistic experimental study has been conducted, highlighting the benefits of the proposed methods in a variety of experimental scenarios, and verifying increased situational awareness ability. The proposed distributed approaches offer high positioning accuracy, outperforming other SOA centralized methods.


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    Title :

    Extending Online 4D Situational Awareness in Connected and Automated Vehicles



    Published in:

    Publication date :

    2024-08-01


    Size :

    5760127 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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