A Cooperative Lane Change Method for Connected and Automated Vehicles Based on Reinforcement Learning

Connected and Automated Vehicle (CAV) is a newgeneration vehicle equipped with advanced on-board sensors, controllers, actuators, and other devices. In recent years, CAVs have gradually become the main trend of vehicle development, and people’s choice of travel mode has begun to change from traditional cars to CAVs. Cellular Vehicle-to-Everything (C-V2X) connects vehicles to everything and allows transportation participants to interconnect through modern communication technologies. In C-V2X method, cooperative lane change is one of the important parts. The challenge of cooperative lane change has two main parts, one is how to determine a safe and efficient passing strategy, and the other is how to determine a reasonable communication method. To solve the challenge, in this paper, we use V2X communication as the basis of vehicle information exchange, transform the two-vehicle lane-changing problem into a Markov Decision Process (MDP), and then solve it through Q-learning and DQN to obtain the ideal lane-changing decision.