Collaborative control method for variable lanes and traffic signals based on deep reinforcement learning

Abstract

The invention discloses a method for collaborative control of variable lanes and traffic signals based on deep reinforcement learning, which includes collecting state observations at intersections, including vehicle data, signal light data, and variable lane data, and inputting them to the neural network after preprocessing ; The neural network is continuously trained and updated based on reinforcement learning until the model converges; optimal control is performed based on the trained neural network, and an optimal control strategy is output. The present invention realizes the coupling control of the variable lane and the traffic signal, and can perform real-time optimal control according to the real-time state of the intersection area, without manual work, and the switching of the variable lane and the control of the traffic signal are completely based on the traffic flow The data is adaptively adjusted, and there is no secondary parking of vehicles, which optimizes the utilization efficiency of time and space resources at signal-controlled intersections.

Description

technical field [0001] The invention relates to the technical field of road traffic control, and more specifically relates to a cooperative self-adaptive optimal control method for variable lanes and traffic signals at signal-controlled intersections in a vehicle-road collaborative environment. Background technique [0002] Signal-controlled intersections are often places where urban road traffic congestion occurs, which has a huge impact on the overall operation of urban traffic. Traffic lights can ensure that vehicles pass through the intersection in an orderly manner from the time perspective, and variable lanes can ensure that vehicles in different directions can efficiently use road space resources from the spatial perspective. [0003] Although both traffic lights and variable lanes can go a long way toward ensuring traffic runs smoothly, there is often a lack of close coordination between them. Since the intersection is a dynamic scene where vehicles pass continuousl...

AI Technical Summary

Problems solved by technology

The two are not well coupled and optimized

In addition, the optimization of one of traffic signals and variable lanes will definitely affect the other, if the other does not change accordingly, it may even reduce the traffic efficiency of the intersection

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