The invention discloses a traffic signal lamp adaptive timing control method based on deep reinforcement learning. The method comprises the following steps: obtaining a real-time traffic state based on a pre-constructed urban intersection model; based on the real-time traffic state, a theoretical optimal action is generated through a pre-trained traffic signal control model based on a depth deterministic strategy gradient algorithm, the theoretical optimal action comprises a plurality of single actions in one-to-one correspondence with the traffic lights, and each single action comprises an action state and an action duration; comparing the theoretical optimal action with a predetermined standard action interval, and correcting the theoretical optimal action according to a comparison result to obtain an actual optimal action; and controlling the traffic signal lamp based on the actual optimal action. The invention provides a traffic signal lamp adaptive timing control method and system based on deep reinforcement learning. The traffic signal lamp adaptive timing control method and system can control traffic signal lamps more flexibly and accurately.
基于深度强化学习的交通信号灯自适应配时控制方法,包括如下步骤:基于预先构建的城市交叉口模型获取实时交通状态;基于实时交通状态通过预先训练好的基于深度确定性策略梯度算法的交通信号控制模型生成理论最优动作,理论最优动作包括多个与交通信号灯一一对应的单体动作,单体动作包括动作状态和动作存续时长;将理论最优动作与预先确定的标准动作区间进行比较,并且根据比较结果对理论最优动作进行修正得到实际最优动作;基于实际最优动作对交通信号灯进行控制。本发明提供一种基于深度强化学习的交通信号灯自适应配时控制方法及系统,能够更加灵活和精确地对交通信号灯进行控制。
Traffic signal lamp adaptive timing control method and system based on deep reinforcement learning
基于深度强化学习的交通信号灯自适应配时控制方法及系统
13.05.2025
Patent
Elektronische Ressource
Chinesisch
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
Europäisches Patentamt | 2024
|Traffic signal lamp timing method, device and equipment based on deep reinforcement learning
Europäisches Patentamt | 2023
|Traffic signal lamp control system and method based on reinforcement learning and dynamic timing
Europäisches Patentamt | 2021
|Adaptive Optimization of Traffic Signal Timing via Deep Reinforcement Learning
DOAJ | 2021
|Europäisches Patentamt | 2022
|