Dissipation of Stop-and-Go Waves of Mixed Autonomous Vehicle Flow with Reinforcement Learning

Traffic congestion is a common phenomenon in cities, and improving traffic efficiency has become an urgent problem to be solved. Many researchers have proposed feasible solutions from different perspectives, such as traffic flow, signal lights, etc. This paper proposes a method of dissipating stop-and-go wave based on reinforcement learning (RL). Specifically, we take the mixed autonomous vehicle flow as the research object, using RL to train the driving strategy of connected autonomous vehicle (CAV), and dissipating the stop-and-go waves in vehicle flow by adjusting the CAV’s driving behavior. We propose the concept of "Equivalent Density Difference" as a model to describe the difference of traffic flow dynamics before and after a specific vehicle within a certain range, and use this index to design RL model. The proposed method combines the advantages of data-driven and model-driven, improving the training efficiency of RL. Experimental results show that this method can increase the system-level speed and improve the stability of the mixed autonomous vehicle flow.