The invention provides an automatic driving track prediction method and system, a storage medium and equipment, and relates to the field of automatic driving, the method is applied to a deep learning network model, and the deep learning network model comprises a VectorNet network, a graph neural network GNN, a FusionNet fusion module and a GRU decoder. By setting a complete deep learning network model, the method aims to improve the ability of recognizing lane structure features in complex urban roads and enhance interaction between data, so that the accuracy of target vehicle trajectory prediction is improved, and the driving safety is ensured.

    本发明提供一种自动驾驶轨迹预测方法、系统、存储介质及设备,涉及自动驾驶领域,方法应用于深度学习网络模型,深度学习网络模型包括VectorNet网络、图神经网络GNN、FusionNet融合模块和GRU解码器。通过设置完整的深度学习网络模型,旨在提高复杂城市道路中,识别车道结构特征的能力,增强数据间的交互,从而提升目标车辆轨迹预测的准确性,确保行车安全。


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    Title :

    Automatic driving track prediction method and system, storage medium and equipment


    Additional title:

    一种自动驾驶轨迹预测方法、系统、存储介质及设备


    Contributors:
    WANG XIAOLIANG (author) / LIAO CHENGLIANG (author) / MA SHUFANG (author) / SONG WEN (author) / HE XIAOMING (author) / PENG HONGRUI (author) / MIN PENG (author)

    Publication date :

    2025-02-07


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06V



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