The invention discloses a traffic signal lamp control method based on a neural symbol system and reinforcement learning, and the method comprises the steps: S1, obtaining vehicle data in real time through a camera, a geomagnetic sensor and a GPS, collecting weather information through a meteorological sensor, obtaining the identifications of workdays, weekends and peak periods through a server, and carrying out the recognition of the workdays, weekends and peak periods; meanwhile, signal lamp information is obtained through a signal lamp controller; s2, processing a camera video stream by using a convolutional neural network, outputting real-time vehicle density and speed distribution of each lane, and constructing a traffic flow feature vector by using sensor data; and S3, mapping the deep learning features into explainable traffic rules by adopting a neural symbol system, and generating constraint conditions in combination with the traffic laws and regulations. According to the method, the perception capability of deep learning, the interpretability of symbol logic and the dynamic optimization capability of reinforcement learning are fused, so that the transparency and real-time adaptability of traffic signal lamp control are improved.
本发明公开了一种基于神经符号系统与强化学习的交通信号灯控制方法,包括:步骤S1,通过摄像头、地磁传感器和GPS实时获取车辆数据,通过气象传感器采集天气信息,通过服务器获得工作日、周末及高峰时段标识,同时通过信号灯控制器获得信号灯信息;步骤S2,采用卷积神经网络处理摄像头视频流,输出各车道的实时车辆密度、速度分布,并利用传感器数据构建交通流特征向量;步骤S3,采用神经符号系统将深度学习特征映射为可解释的交通规则,结合交通法规生成约束条件。本发明通过融合深度学习的感知能力、符号逻辑的可解释性和强化学习的动态优化能力,提升了交通信号灯控制的透明性和实时适应性。
Traffic signal lamp control method based on neural symbol system and reinforcement learning
一种基于神经符号系统与强化学习的交通信号灯控制方法
2025-05-16
Patent
Electronic Resource
Chinese
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