In the field of autonomous driving, collaborative perception has emerged as a promising solution for augmenting the capabilities of individual sensors by enabling vehicles to share their sensor information across each other, thereby enhancing their situational awareness. This paper addresses the limitations of classical perception in autonomous vehicles by proposing a novel intermediate collaborative perception methodology employing graph attention network (GAT) to incorporate multiple feature maps and to selectively emphasize important regions within the feature maps. We construct the graph structure as a set of nodes embedding the ego and the neighboring connected vehicles feature maps, as well as establish edge weights between those nodes based on their relationship to each other which is defined by the attention coefficients. The proposed approach leverages both channel and spatial attention-based aggregation and enables the model to determine inter-feature map relationships at a specific channel and spatial regions, while adaptively highlighting the informative regions. This adaptive highlighting mechanism directs the aggregation algorithm towards the most informative areas within the ego and the received feature maps, thereby enhancing the representation power of the ego vehicle’s feature map leading to improved precision in object detection. We quantitatively and qualitatively evaluate the performance of our proposed approach against existing state-of-the-art in collaborative perception. We validate our methodology using V2XSim, a large-scale multi-agent perception dataset. The results demonstrate that our methodology achieves superior performance in enhancing object detection average precision.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Graph Attention Based Feature Fusion For Collaborative Perception


    Contributors:


    Publication date :

    2024-06-02


    Size :

    2290566 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Real-Time Railway Scene Perception Based on Fast Reconvolution Skip Fusion and Dual Attention Ladder Feature Enhancement

    Bai, Jie / Qin, Yong / Cao, Zhiwei et al. | Transportation Research Record | 2025


    A late fusion framework for multivehicle collaborative perception

    Li, Zengwen / Gao, Yingying / Lv, Huaxin et al. | SPIE | 2024


    Vehicle infrastructure collaborative perception fusion method and system

    ZHAO YIMING / MA ZE / KUMAKICHI et al. | European Patent Office | 2023

    Free access


    Traffic flow prediction method of space-time attention graph convolutional network based on multi-feature fusion

    CHEN YAJUN / DING ZHIMING / GUO LIMIN | European Patent Office | 2023

    Free access