Variable lane and traffic signal cooperative control method based on deep reinforcement learning

Abstract

The invention discloses a variable lane and traffic signal cooperative control method based on deep reinforcement learning, and the method comprises the steps: collecting an intersection state observation value which comprises vehicle data, signal lamp data and variable lane data, carrying out the preprocessing, and inputting the preprocessed data to a neural network; continuously training and updating the neural network based on reinforcement learning until the model converges; and performing optimal control based on the trained neural network, and outputting an optimal control strategy. Coupling control over the variable lane and the traffic signal is achieved, real-time optimal control can be conducted according to the real-time state of the intersection area, manual operation is not needed, switching of the variable lane and control over the traffic signal are adjusted in a self-adaptive mode completely according to the traffic flow data, secondary parking of vehicles is not caused, and the time-space resource utilization efficiency of the signal control intersection is optimized.

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

Images