Connected and Automated Vehicles (CAVs) rely heavily on on-board sensing technologies and machine learning (ML) algorithms to perceive the surrounding environment and behave accordingly. Hence, the speed and accuracy of ML models that detect surrounding objects is crucial to ensure road safety. In this work, we propose to ensure detection accuracy while maintaining efficiency by adapting the object detection model to the specific driving context where the CAV is traveling. Fine-tuning has been applied to achieve this objective. With the support of the Vehicular Digital Twin, paired to the CAV, the model that best suits the driving context is identified and loaded into local vehicular computing facilities. Preliminary experimental results highlight the superiority of our proposal compared to the most recent legacy YOLO implementations.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Digital Twin-assisted Context-aware Object Detection for Connected and Automated Vehicles


    Beteiligte:


    Erscheinungsdatum :

    02.06.2025


    Format / Umfang :

    380792 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Digital Twin-Based Cooperative Driving at Roundabouts for Connected and Automated Vehicles

    Li, Zihao / Li, Shuaijie / Abdelraouf, Amr et al. | IEEE | 2024


    Unknown-Aware Hierarchical Object Detection in the Context of Automated Driving

    Zhou, Jingxing / Wandelburg, Nick / Beyerer, Jurgen | IEEE | 2023


    CT2‐MDS: Cooperative trust‐aware tolerant misbehaviour detection system for connected and automated vehicles

    Liu, Ying / Xue, Hongwei / Zhuang, Weichao et al. | Wiley | 2022

    Freier Zugriff

    Congestion-aware heterogeneous connected automated vehicles cooperative scheduling problems at intersections

    Chowdhury, Farzana R. / Wang, Peirong (Slade) / Li, Pengfei (Taylor) | Taylor & Francis Verlag | 2023


    CT2‐MDS: Cooperative trust‐aware tolerant misbehaviour detection system for connected and automated vehicles

    Ying Liu / Hongwei Xue / Weichao Zhuang et al. | DOAJ | 2022

    Freier Zugriff