The invention discloses a traffic light and intelligent network connection vehicle cooperative control method in an intersection scene. According to the method, a deep reinforcement learning model is adopted, a reward function for optimizing vehicle fuel consumption and queuing time delay is designed, and a cooperative control problem is expressed as a partial Markov decision process; then, traffic lights and intelligent network connection automobiles are modeled into two types of intelligent agents, and the two types of intelligent agents obtain a joint state space through real-time interaction state information; thirdly, inputting the joint state space into a trained deep reinforcement learning model to obtain an agent joint action space; and finally, according to the combined action space, cooperative control is carried out on the two intelligent agents of the traffic light and the intelligent network connection automobile. The control parameters comprise the traffic light phase and the longitudinal movement acceleration and deceleration of the intelligent network connection vehicle. Compared with an existing method, the method has the main advantages that the vehicle passing efficiency at the intersection is remarkably improved, and meanwhile the vehicle exhaust emission and oil consumption can be greatly reduced.
本发明公开一种交叉路口场景下交通灯与智能网联车辆协同控制的方法。该方法采用深度强化学习模型,通过设计优化车辆油耗与排队时延的奖励函数,将协同控制问题表述为部分马尔可夫决策过程;然后,将交通灯与智能网联汽车建模为两类智能体,这两类智能体通过实时交互状态信息,得到联合状态空间;接着,将联合状态空间输入到训练好的深度强化学习模型,得到智能体联合动作空间;最后,根据联合动作空间,对交通灯与智能网联汽车两类智能体进行协同控制。控制参量包括:交通灯相位和智能网联车辆纵向运动加、减速度。本发明相较于已有方法的主要优势在于,该方法在显著提升交叉路口车辆通行效率的同时,还能够大幅度减少车辆尾气排放量和油耗。
Cooperative control method for traffic lights and intelligent network connection vehicles in intersection scene
一种交叉口场景下交通灯与智能网联车辆协同控制方法
2024-09-10
Patent
Electronic Resource
Chinese
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