Perceiving the surrounding environment is critical to enable cooperative driving automation, which is regarded as a transformative solution to improving our transportation system. Cooperative perception, by cooperating information from spatially separated nodes, can innately unlock the bottleneck caused by physical occlusions and has become an important research topic. Although cooperative perception aims to resolve practical problems, most of the current research work is designed based on the default assumption that the communication capacities of collaborated perception entities are identical. In this work, we introduce a fundamental approach - Dynamic Feature Sharing (DFS) - for cooperative perception from a more pragmatic context. Specifically, a DFS-based cooperative perception framework is designed to dynamically reduce the feature data required for sharing among the cooperating entities. Convolution-based Priority Filtering (CPF) is proposed to enable DFS under different communication constraints (e.g., bandwidth) by filtering the feature data according to the designed priority values. Zero-shot experiments demonstrate that the proposed CPF method can improve cooperative perception performance by approximately +22% under a dynamic communication-capacity condition and up to +130% when the communication bandwidth is reduced by 90 %.


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    Titel :

    Dynamic Feature Sharing for Cooperative Perception from Point Clouds


    Beteiligte:
    Bai, Zhengwei (Autor:in) / Wu, Guoyuan (Autor:in) / Barth, Matthew J. (Autor:in) / Liu, Yongkang (Autor:in) / Sisbot, Emrah Akin (Autor:in) / Oguchi, Kentaro (Autor:in)


    Erscheinungsdatum :

    2023-09-24


    Format / Umfang :

    3385731 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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