In the rapidly advancing realm of Connected Autonomous Vehicles (CAVs), achieving reliable and precise positioning is of paramount importance. This paper presents a comprehensive approach integrating vehicular sensing, communication, and advanced filtering techniques to enhance vehicle positioning in urban areas. By leveraging LiDAR point clouds along with a light and accurate object detector, we create cohesive environmental sensing that improves situational awareness in autonomous systems. Central to our methodology is the integration of the Labeled Multi-Bernoulli Mixture (LMBM) filter, which offers a probabilistic framework for dynamic state estimation in environments characterized by high uncertainty and clutter. In turn, enhanced object locations are exploited as anchors for vehicular self-localization via an Extended Kalman filter (EKF). Our experimental results show that the proposed solution significantly enhances vehicular positioning accuracy.


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    Titel :

    Cooperative LiDAR-Aided Self-localization of CAVs in Real Urban Scenario


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Mastinu, Giampiero (Herausgeber:in) / Braghin, Francesco (Herausgeber:in) / Cheli, Federico (Herausgeber:in) / Corno, Matteo (Herausgeber:in) / Savaresi, Sergio M. (Herausgeber:in) / Adas, Akif (Autor:in) / Barbieri, Luca (Autor:in) / Morri, Pietro (Autor:in) / Mentasti, Simone (Autor:in) / Awasthi, Satyesh (Autor:in)

    Kongress:

    Advanced Vehicle Control Symposium ; 2024 ; Milan, Italy September 01, 2024 - September 05, 2024



    Erscheinungsdatum :

    04.10.2024


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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