This paper addresses the challenges of the roadside sensing infrastructure in providing reliable vehicle classification and positioning data, and sharing said data with connected and automated vehicles. For this purpose, a novel sensor fusion model is proposed, fusing data streams from traffic classification radar and video camera, leveraging the best characteristics of each sensor. The goal is to extend vehicles' sensing horizon while enhancing object classification performance and disseminating data with neighboring vehicles via Collective Perception Message (CPM). Our model incorporates two Artificial intelligence (AI) models for object identification and data fusion. Experimental results indicate a 23.73% improvement over the native radar classification under optimal conditions, showcasing the model's efficacy. Comprehensive data analysis reveals the model's resilience under diverse conditions, outperforming radar and independent camera classifications. The fused model corrects camera errors, achieving superior accuracy, especially in nighttime scenarios. This research contributes to safer and more efficient cooper-ative driving experiences, demonstrating the effectiveness of the sensor fusion approach in varying environmental conditions.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Sensor Fusion for Improved Cooperative Perception in CCAM


    Beteiligte:
    Pinho, Hugo (Autor:in) / Ferreira, Joaquim (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    7517331 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    CCAM legislation study: The Greek pathway

    Patatouka, Eleni / Antonakopoulou, Anna / Amditis, Angelos et al. | IEEE | 2025



    Open and Modular Service-Oriented CCAM Architecture

    Bolovinou, Anastasia / Spanos, Georgios / Lalas, Antonios et al. | Springer Verlag | 2024

    Freier Zugriff

    A Fail-Safe Decision Architecture for CCAM Applications

    Rodríguez-Arozamena, Mario / Matute, Jose / Pérez, Joshué et al. | Springer Verlag | 2025

    Freier Zugriff

    AITHENA: Towards a Trustworthy AI for CCAM Development

    Otaegui, Oihana / Nieto, Marcos / Rasca, Sinziana Ioana et al. | Springer Verlag | 2025

    Freier Zugriff