Deep Reinforcement Learning for Trajectory Control of Connected and Automated Vehicles at a Mixed-Traffic Intersection

Vehicle trajectory control has garnered significant interest due to the potential of connected and automated vehicles (CAVs) to enhance traffic efficiency and reduce accidents. Effective vehicle control is crucial for autonomous driving and has traditionally been addressed through motion control methods. In recent years, a growing focus has been on deep learning (DL), particularly deep reinforcement learning (DRL), to optimize CAV control. These advanced techniques enable CAVs to learn and adapt to their environment, further advancing autonomous driving capabilities. However, much research primarily focuses on simple and ideal scenarios involving fully CAV environments. Although there is research on mixed-traffic scenarios with both CAVs and human-driven vehicles (HDVs), it often neglects other road participants such as pedestrians and cyclists. Many studies on trajectory control focus on enhancing traffic efficiency and reducing vehicle emissions. However, it is equally essential to consider safety improvements in mixed-traffic environments. Addressing the aforementioned issues, this paper proposes a DRL-based trajectory control approach for CAVs at a mixed-traffic intersection involving CAVs, HDVs, and pedestrians. CAVs learn policies for various actions to reach their destinations. The acceleration of the CAVs is optimized using the deep deterministic policy gradient (DDPG) algorithm to maximize a reward function that accounts for safety, energy efficiency, and traffic efficiency. Our approach is tested using the Simulation of Urban Mobility (SUMO) platform to model realistic intersection dynamics. Results demonstrated that the proposed method significantly improved traffic efficiency and reduced fuel consumption, while notably decreasing both vehicle–vehicle and vehicle–pedestrian conflicts. This research highlights the importance of incorporating diverse road users into CAV control strategies and contributes to the development of safer, greener, and more inclusive traffic management solutions.