The invention provides a highland cooperative control method fusing a graph convolutional neural network and reinforcement learning, through controlling urban road traffic signal lamps, an expressway ramp signal machine and a variable speed limit unit, expressway traffic congestion is relieved, the urban traffic passing efficiency is improved, road driving vehicles are controlled by means of the signal lamps and the speed limit unit, and the traffic efficiency is improved. Traffic flow scheduling optimization in a global road network is achieved; the whole structure adopts a distributed cooperative control framework, is controlled by a decision-making agent, and performs autonomous decision-making and adjustment according to real-time traffic conditions, so that the adaptability and robustness of the system are enhanced, and the defects of large data calculation amount, difficulty in processing, control strategy lag and the like caused by traditional centralized control are effectively avoided; the graph convolutional neural network model is constructed to aggregate the feature information of the peripheral decision intelligent agent, and the control weight is dynamically allocated, so that the information sharing capability is enhanced, the suboptimal decision caused by lack of comprehensive perception of the surrounding environment is prevented, and the cooperation efficiency of the whole system is improved.
本发明提供了一种融合图卷积神经网络和强化学习的高地协同控制方法,通过控制城市道路交通信号灯、高速公路匝道信号机和可变限速单元,缓解高速公路交通拥堵并提升城市交通通行效率,借助信号灯与限速单元控制道路行驶车辆,达到全局路网中的车流调度优化;整体结构采用分布式协同控制框架,由决策智能体控制,根据实时交通情况自主决策与调整,从而增强系统的自适应性和鲁棒性,有效避免了传统集中式控制带来的数据计算量大、处理难、控制策略滞后等缺陷;构建图卷积神经网络模型以聚合周边决策智能体的特征信息,并动态分配控制权重,增强了信息共享能力,防止因缺乏对周边环境的全面感知而导致的次优决策,从而提高整体系统的协同效率。
Graph convolutional neural network and reinforcement learning fused highland cooperative control method
融合图卷积神经网络和强化学习的高地协同控制方法
2025-03-07
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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