Cooperative perception is an effective way for connected autonomous vehicles (CAVs) to surpass their sensing limitations, by sharing information like intermediate features extracted from images or point clouds with each other. To reduce bandwidth consumption, feature filtering is adopted by existing methods to share only the most valuable information. However, these methods assume that the features on the same channel across all spatial regions or those in the same spatial regions across all the channels are equally important. This assumption results in coarse-grained feature filtering, which greatly decreases the cooperative perception performance. To solve this problem, this paper proposes a fine-grained channel-spatial feature filtering scheme, named SwissCheese, for communication-efficient cooperative perception. The key idea of SwissCheese is to exploit the disparity in semantic information on features between different spatial regions on different channels. Specifically, a fine-grained collaborative attention module is developed to jointly learn fine-grained attention along the channel-spatial dimensions. Moreover, a dual-dimensional feature selection strategy that selects sparse features for transmission based on the current available bandwidth is designed to achieve optimal perception performance. Experiment results show that SwissCheese significantly reduces the transmission data size by 90% with a subtle loss in perception performance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    SwissCheese: Fine-Grained Channel-Spatial Feature Filtering for Communication-Efficient Cooperative Perception


    Beteiligte:
    Xie, Qi (Autor:in) / Zhou, Xiaobo (Autor:in) / Hong, Tianyu (Autor:in) / Qiu, Tie (Autor:in) / Qu, Wenyu (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.12.2024


    Format / Umfang :

    5672784 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Fine-Grained Feature Perception for Unmanned Aerial Vehicle Target Detection Algorithm

    Shi Liu / Meng Zhu / Rui Tao et al. | DOAJ | 2024

    Freier Zugriff

    INSTANTANEOUS PERCEPTION OF FINE-GRAINED 3D MOTION

    ZHUANG BINGBING / CHANDRAKER MANMOHAN / LIU DI | Europäisches Patentamt | 2025

    Freier Zugriff

    INSTANTANEOUS PERCEPTION OF FINE-GRAINED 3D MOTION

    ZHUANG BINGBING / CHANDRAKER MANMOHAN / LIU DI | Europäisches Patentamt | 2025

    Freier Zugriff

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

    Lin, Chunmian / Tian, Daxin / Duan, Xuting et al. | IEEE | 2024


    Multi-View and Multi-Scale Fine-Grained Vehicle Classification with Channel Convolution Feature Fusion

    Silva, Bruno / Oliveira, Roberto / Barbosa-Anda, Francisco Rodolfo et al. | IEEE | 2021