Enhancing Cooperative Lane-Changing for Intelligent Connected Vehicles: A DDPG-Based Approach

Current research on lane-changing for intelligent connected vehicles (ICVs) primarily focuses on the control of a single ICV. Even studies on cooperative lane-changing among multiple ICVs often use fixed control algorithms, which are difficult to adapt to constantly changing traffic conditions. This paper proposes a method based on the deep deterministic policy gradient (DDPG) model. The DDPG algorithm has strong generalization capabilities, enabling it to handle complex traffic scenarios. Combined with the design of innovative reward functions, it successfully achieves the control of cooperative lane-changing for multiple ICVs. The novelty of this model is mainly reflected in the implementation of multiple ICVs control and cooperative strategies through the design of a speed reward function. Specifically, when two or more ICVs come within a 15-m range, a collaborative strategy is implemented. In this strategy, the original speeds of the vehicles involved are replaced with their average speed. This average speed is then used to compute the velocity reward. Moreover, an additional headway reward is introduced when two vehicles are on the same lane with a distance of less than 10[Formula: see text]m between them, further optimizing lane-changing decisions and enhancing driving efficiency. The innovative approach described in this paper has been validated through simulations in the highway-env simulation environment. The model’s effectiveness was tested under various conditions, including different road densities and numbers of ICVs. Results indicate that with an increasing number of episodes, the model consistently enhances both the fleet’s and each individual vehicle’s average speed and steps. It also ensures that the headway is maintained within a desired range of 10–20[Formula: see text]m. This paper showcases the potential applications of a new DDPG-based multi-networked vehicular cooperative lane-changing decision model across different scenarios. These findings provide important reference values for future research and development in intelligent transportation systems (ITS), particularly in optimizing vehicle cooperative behaviors and enhancing traffic efficiency. This paper emphasizes the model’s potential to significantly impact the management and operation of future vehicular networks, paving the way for more sophisticated and efficient transportation systems.