The invention provides a vehicle-road cooperative sensing method based on deep reinforcement learning, and the method comprises the steps: obtaining the positions of all CAVs through a road side unit (RSU) through a cooperative object selection method based on the RSU, distributing an optimal cooperative object for each CAV through employing a DQN network, and enabling the CAVs to achieve the cooperative object after obtaining an object needing to be cooperated, and selecting an optimal three-dimensional detection frame and a corresponding confidence score by using a DQN network based on invalid action shielding, and sending the optimal three-dimensional detection frame and the corresponding confidence score to a collaborative object to realize vehicle-road collaborative perception.

    本发明提供一种基于深度强化学习的车路协同感知方法,通过基于路侧单元RSU的协作对象选择方法,由路侧单元RSU获取所有自动驾驶车辆CAV的位置,使用DQN网络为每辆自动驾驶车辆CAV分配最佳的协作对象,自动驾驶车辆CAV获知需要协作的对象后,使用基于无效动作屏蔽的DQN网络选择最佳的三维检测框和对应的置信度分数,将最佳的三维检测框和对应的置信度分数发送给协作对象以实现车路协同感知。


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    Title :

    Vehicle infrastructure collaborative awareness method based on deep reinforcement learning


    Additional title:

    一种基于深度强化学习的车路协同感知方法


    Contributors:
    ZHENG HAIFENG (author) / LIU YANG (author) / XU FAN (author) / FENG XINXIN (author)

    Publication date :

    2025-03-04


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

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



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