The future trajectory prediction of heterogeneous traffic-agents for autonomous vehicles in mixed traffic scene is of great significance for safe and reliable driving. Thus, we propose the Multi-View Adaptive Hierarchical Spatial Graph Convolution Network (MVHGN) to predict the future trajectories of heterogeneous traffic-agents. Firstly, multiple logical correlations are obtained based on the time series data of traffic-agents and a multi-view logical network is constructed. The multi-view logical feature extraction is realized based on the graph convolution module. Then, combining the multi-view logical features and the adaptive spatial topology network, the logical-physical features at the micro level are mined through the graph convolution module; based on the logical-physical features at the micro level and the regional clustering network at the macro level, the global logical-physical features are obtained. Finally, the model predicts the future trajectories of traffic-agents based on the encoder-decoder structure of the GRU. For the Apolloscape trajectory data set, the performance of our proposed method MVHGN is better than that of the comparison models.


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    Title :

    MVHGN: Multi-View Adaptive Hierarchical Spatial Graph Convolution Network Based Trajectory Prediction for Heterogeneous Traffic-Agents


    Contributors:
    Xu, Dongwei (author) / Shang, Xuetian (author) / Peng, Hang (author) / Li, Haijian (author)


    Publication date :

    2023-06-01


    Size :

    1730745 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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