Precise multi-vessel trajectory prediction (MVTP) in intelligent collision avoidance systems is a crucial yet challenging task due to the dynamic and continuous spatio-temporal dependencies between different vessels. However, most existing frameworks utilize graph structures with shared nodes to capture static correlations and employ sophisticated mechanisms to learn short-term trends hidden in trajectories, which fail to provide sufficient insight into the dynamic interactions and long-term patterns among vessels, limiting prediction accuracy and effectiveness. To address these pivotal issues, we propose a Dynamic Spatio-Temporal Graph Attention Network (DSTGAN) for MVTP, catering to intricate interactions modeling with long- and short-term motion pattern perception. In the spatial dimension, we present a vessel spatial graph encoder (VSGE) to learn dynamic interactions, which adaptively reconstructs the adjacency matrix from historical vessel data instead of constructing a fixed graph according to vessel proximity. Also, we introduce attribute-aware graph attention in VSGE to guide customized information propagation, and an adaptive neighbor sampling strategy to control the quality of vessel feature aggregation. For the temporal dimension, a vessel temporal graph fusion module (VTGF) is designed to enhance the perception of temporal dependencies at both local and global scales. To prevent information leakage, we propose masked time-aware attention, which automatically masks the features of future vessel nodes during attention computation. Furthermore, we incorporate a residual control block (RCB) to improve noise resilience and model convergence. Finally, the multi-vessel trajectory prediction module combines spatio-temporal features from VSGE and VTGF for subsequent prediction. Extensive experiments on three real-world Automatic Identification System (AIS) datasets demonstrate the superior performance of our method in comparison to state-of-the-art approaches.


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    Titel :

    Learning Dynamic Interactions and Long-Term Patterns With Spatio-Temporal Graphs for Multi-Vessel Trajectory Prediction


    Beteiligte:
    Zhang, Xiliang (Autor:in) / Liu, Jin (Autor:in) / Chen, Kejie (Autor:in) / Gong, Peizhu (Autor:in) / Liu, Yuxin (Autor:in) / Wu, Zhongdai (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.12.2024


    Format / Umfang :

    5132544 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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