基于多路口车辆感知预测的协同信号配时技术

Cooperative Traffic Signal Control with Vehicle Perception Prediction in Multi-Intersection

为了缓解大城市的交通拥堵现状,交叉口信号灯配时的研究越来越有必要。普遍的交通信号配时技术,是基于单路口、传统车辆检测方法而设计的,没有考虑影响实际交通状况的流量,存在配时不准确、不智能的局限性。通过基于强化学习的深度Q网络,提出基于多路口车辆感知预测的协同信号配时技术,将每个路口建模为一个代理,每个代理被训练从道路环境接受交通状态并采取最佳行动。实验表明,该方法不仅可以有效地进行交通流量预测,解决多路口协同的信号灯配时问题,还可以提高配时技术的智能性。

In order to alleviate the current situation of traffic congestion in large cities,it is more and more necessary to study the signal timing at intersections.The universal traffic signal timing technology is designed based on single intersection and traditional vehicle detection method.It does not consider the flow affectsing the actual traffic situation,and has the limitations of inaccurate timing and intelligence.Based on the deep Q network of reinforcement learning,this paper proposes a cooperative signal timing technology based on multi intersection vehicle perception prediction.This technology will model each intersection as an agent,and each agent is trained to accept the traffic status from the road environment and take the best action.Experiments show that this method can not only effectively predict traffic flow,solve the problem of multi intersection coordinated signal timing,but also improve the intelligence of timing technology.