The invention relates to an environmental perception traffic signal control method based on reinforcement learning, and belongs to the technical field of computers. According to the method, traffic signals are intelligently regulated and controlled by sensing environment information and traffic states and combining road states. Firstly, a traffic signal regulation and control strategy is learned in a simulation environment by using a reinforcement learning algorithm, and then the obtained regulation and control strategy is applied to a real scene. The specific implementation comprises the steps of collecting environmental perception data, constructing a simulated traffic environment, configuring a control strategy, setting a typical traffic scene, collecting environmental information, fusing features, setting performance indexes, reinforcing learning optimization, applying a real scene, establishing a feedback mechanism and the like. Through combination of reinforcement learning and environment perception technologies, the method can dynamically adapt to traffic demands in different time and environments, effectively alleviates traffic congestion, improves traffic operation efficiency, and provides an effective scheme for improving urban traffic conditions.
本发明涉及一种基于强化学习的环境感知交通信号控制方法,属于计算机技术领域。该方法通过感知环境信息和交通状态,结合道路状态,智能调控交通信号。首先,在模拟环境中利用强化学习算法学习调控交通信号策略,再将得到的调控策略运用于现实场景。具体实施包括收集环境感知数据、构建仿真交通环境、配置控制策略、设置典型交通场景、收集环境信息、特征融合、性能指标设定、强化学习优化、真实场景应用、反馈机制建立等步骤。本发明通过结合强化学习和环境感知技术,能够动态适应不同时间和环境下的交通需求,有效缓解交通拥堵,提高交通运行效率,为改善城市交通状况提供了一种有效方案。
Environmental perception traffic signal control method based on reinforcement learning
一种基于强化学习的环境感知交通信号控制方法
2025-02-21
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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