This paper proposes a cooperative prediction framework named Co-HTTP which enables collaboration between vehicle-side prediction (Veh-Pred) and infrastructure-side prediction (Infra-Pred). First, the cleaned infrastructure's historical data is conveyed to the autonomous vehicles (AVs) for initial collaboration. Then, this paper predicts the target vehicles based on the driving intentions of AVs besides historical trajectories. Additionally, the intentions of agents around are all embedded into a heterogeneous graph neural network (GNN), updated by Transformer layers in a heterogeneous way, to further enhance vehicle-infrastructure cooperation. Finally, Co-HTTP is evaluated on V2X-Seq dataset, demonstrating that its prediction performance is better than the current state-of-the-art method on this dataset. As a learnable cooperative prediction paradigm, Co-HTTP will contribute to the improvement of autonomous vehicles.


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    Title :

    Co-HTTP: Cooperative Trajectory Prediction with Heterogeneous Graph Transformer for Autonomous Driving*


    Contributors:
    Zhang, Xinyu (author) / Zhou, Zewei (author) / Ji, Yangjie (author) / Xing, Jiaming (author) / Wang, Zhaoyi (author) / Huang, Yanjun (author)


    Publication date :

    2024-09-24


    Size :

    8145849 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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