The invention relates to an automatic driving scene controllable generation method based on knowledge enhancement, and the method comprises the following steps: building an interactive representation model of traffic participants, carrying out the joint motion prediction of all traffic participants in an automatic driving scene through the interactive representation model, and obtaining the predicted joint motion information; establishing guidance characterization of an automatic driving scene through traffic prior knowledge; and establishing a diffusion model for controllable generation of the automatic driving scene, embedding the guide representation into the diffusion model, and disturbing the joint action information under the constraint of the guide representation to generate a diversified automatic driving scene meeting actual requirements. Compared with the prior art, the method has the advantages of high authenticity, high controllability, high safety, high real-time performance and the like.

    本发明涉及一种基于知识增强的自动驾驶场景可控生成方法,包括以下步骤:建立交通参与者的交互表征模型,通过交互表征模型对自动驾驶场景中的各个交通参与者进行联合运动预测,得到预测的联合动作信息;通过交通先验知识建立自动驾驶场景的引导表征;建立用于自动驾驶场景可控生成的扩散模型,将引导表征嵌入扩散模型,在引导表征的约束下,对联合动作信息进行扰动,生成符合实际需求的多样化自动驾驶场景。与现有技术相比,本发明具有真实性强、可控性强、安全性高、实时性强等优点。


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    Titel :

    Automatic driving scene controllable generation method based on knowledge enhancement


    Weitere Titelangaben:

    一种基于知识增强的自动驾驶场景可控生成方法


    Beteiligte:
    GUO LULU (Autor:in) / SHAN CE (Autor:in) / SUN MENGGE (Autor:in) / CHEN HONG (Autor:in)

    Erscheinungsdatum :

    25.04.2025


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



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