The invention discloses a reinforcement learning efficient optimization method for safe passing of left-turning vehicles at a non-signalized intersection. The method integrates scene risk prediction and optimizes a reinforcement learning experience storage and playback mechanism. The method comprises the following steps: firstly, establishing a reinforcement learning model for left turning of vehicles at a non-signalized intersection, and designing a scene risk evaluator and a dual-experience playback pool architecture; secondly, in the reinforcement learning model training process, a scene risk evaluator evaluates a scene risk coefficient of each environment step in real time, a common scene and a risk scene are distinguished according to the scene risk coefficient, and experience samples are stored in two experience playback pools respectively; thirdly, the intelligent agent extracts experience samples from the two experience playback pools according to a set proportion, and particularly extracts a probability distribution function of a scene risk coefficient according to samples in the risk experience playback pools; and finally, repeating the above steps to carry out cyclic training, and continuously optimizing the model until an optimal left-turn vehicle safe passing strategy at the non-signalized intersection is obtained.
无信号交叉口左转车辆安全通行的强化学习高效优化方法,该方法融合场景风险预测,优化强化学习经验存储与回放机制。首先,本发明建立一个无信号交叉口车辆左转的强化学习模型,设计一个场景风险评估器以及一个双经验回放池架构。其次,在强化学习模型训练过程中,场景风险评估器实时评估每个环境步的场景风险系数,并据此区分普通场景与风险场景,将经验样本分别存储在两个经验回放池。再次,智能体按设定比例从两个经验回放池中抽取经验样本,特别是风险经验回放池中的样本抽取依据场景风险系数的概率分布函数;最后,重复以上步骤进行循环训练,不断优化模型,直至获得最优的无信号交叉口左转车辆安全通行策略。
Reinforced learning efficient optimization method for safe passing of left-turning vehicles at non-signalized intersection
无信号交叉口左转车辆安全通行的强化学习高效优化方法
02.08.2024
Patent
Elektronische Ressource
Chinesisch
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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