The invention discloses an automatic driving lane keeping decision-making method based on deep reinforcement learning, and the method specifically comprises the following steps: 1, sequentially building a state space and an action space, designing a composite reward function, and improving an experience playback algorithm; and step 2, setting corresponding training parameters, collecting training data, training the deep reinforcement learning model, and then testing each model. The method shows remarkable superiority in a lane keeping decision task, can adapt to various weather and traffic conditions, improves the lane keeping capability of the automatic driving vehicle in a complex environment, and has a wide application prospect.

    本发明公开了基于深度强化学习的自动驾驶车道保持决策方法,具体按照以下步骤实施:步骤1、依次建立状态空间、动作空间、设计复合奖励函数,并改进经验回放算法;步骤2、设定相应的训练参数,收集训练数据,并对深度强化学习模型进行训练,之后对各模型进行测试。本发明在车道保持决策任务中表现出显著的优越性,能够适应多种天气和交通条件,提高了自动驾驶车辆在复杂环境中的车道保持能力,具备广泛的应用前景。


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    Titel :

    Automatic driving lane keeping decision-making method based on deep reinforcement learning


    Weitere Titelangaben:

    基于深度强化学习的自动驾驶车道保持决策方法


    Beteiligte:
    LIU HAOLIN (Autor:in) / ZHANG XIAOHUI (Autor:in) / BAI WENQI (Autor:in) / WANG XIAOJUAN (Autor:in)

    Erscheinungsdatum :

    10.01.2025


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / 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



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