The invention discloses a CAV speed guidance system based on deep reinforcement learning. The CAV speed guidance system comprises a vehicle-mounted system which detects vehicle driving data and vehicle state information data; the roadside monitoring system detects a driving environment, and vehicle information data and pedestrian information data on different roads, and matches the pedestrian and vehicle information data with a lane-level road link to obtain a data packet; the vehicle-road cooperative cloud computing center processes and stores the received data and sends the processed data to the mobile edge computing center; and the mobile edge calculation center performs comprehensive risk assessment on the road accident risk, constructs a Markov decision process and a speed guidance model of a multi-target additional reward function, constructs the multi-target additional reward function to calculate the optimal guidance speed for the CAV, and sends the optimal guidance speed of the CAV to the vehicle-mounted system. The problem of communication delay is solved; and a multi-target environment reward mechanism is comprehensively integrated, so that the CAV guide speed, the traffic safety, the traffic stability and the traffic efficiency are improved.
本发明公开了一种基于深度强化学习的CAV速度引导系统,包括:车载系统检测车辆行驶数据和车辆状态信息数据;路侧监控系统检测行车环境、不同道路上的车辆信息数据和行人信息数据,将行人及车辆信息数据与车道级道路链路进行匹配,得到数据包;车路协同云计算中心对接收的数据进行处理和存储,将处理后的数据发送给移动边缘计算中心;移动边缘计算中心对道路事故风险进行综合风险评估,构建多目标附加奖励函数的马尔科夫决策过程和速度引导模型,构建多目标附加奖励函数为CAV计算最优引导速度,将CAV最优引导速度发送给车载系统。解决了通信延迟问题;全面综合多目标环境奖励机制,提升CAV引导速度对交通安全性、稳定性及通行效率。
CAV speed guidance system and method based on deep reinforcement learning
一种基于深度强化学习的CAV速度引导系统及方法
2024-02-27
Patent
Elektronische Ressource
Chinesisch
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