Collaborative perception aims for a holistic perceptive construction by leveraging complementary information from nearby connected automated vehicle (CAV), thereby endowing the broader probing scope. Nonetheless, how to aggregate individual observation reasonably remains an open problem. In this article, we propose a novel vehicle-to-vehicle perception framework dubbed V2VFormer with Transformer-based Collaboration (CoTr). Specifically. it re-calibrates feature importance according to position correlation via Spatial-Aware Transformer (SAT), and then performs dynamic semantic interaction with Channel-Wise Transformer (CWT). Of note, CoTr is a light-weight and plug-in-play module that can be adapted seamlessly to the off-the-shelf 3D detectors with an acceptable computational overhead. Additionally, a large-scale cooperative perception dataset V2V-Set is further augmented with a variety of driving conditions, thereby providing extensive knowledge for model pretraining. Qualitative and quantitative experiments demonstrate our proposed V2VFormer achieves the state-of-the-art (SOTA) collaboration performance in both simulated and real-world scenarios, outperforming all counterparts by a substantial margin. We expect this would propel the progress of networked autonomous-driving research in the future.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    V2VFormer: Vehicle-to-Vehicle Cooperative Perception With Spatial-Channel Transformer


    Beteiligte:
    Lin, Chunmian (Autor:in) / Tian, Daxin (Autor:in) / Duan, Xuting (Autor:in) / Zhou, Jianshan (Autor:in) / Zhao, Dezong (Autor:in) / Cao, Dongpu (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.02.2024


    Format / Umfang :

    4981439 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Learning for Vehicle-to-Vehicle Cooperative Perception Under Lossy Communication

    Li, Jinlong / Xu, Runsheng / Liu, Xinyu et al. | IEEE | 2023


    Inter-vehicle object association for cooperative perception systems

    Rauch, Andreas / Maier, Stefan / Klanner, Felix et al. | IEEE | 2013


    Leveraging Temporal Contexts to Enhance Vehicle-Infrastructure Cooperative Perception

    Zhong, Jiaru / Yu, Haibao / Zhu, Tianyi et al. | IEEE | 2024


    Multi-level hybrid vehicle-to-anything communications for cooperative perception

    ALTINTAS ONUR / HIGUCHI TAKAMASA | Europäisches Patentamt | 2020

    Freier Zugriff