Autonomous Driving Decision Model based on Reinforcement Learning Algorithm in the Environment of Intelligent Connected Vehicles

With the rapid development of autonomous driving technology, how to achieve safe, efficient and comfortable vehicle autonomous decision-making in complex traffic environments has become a key challenge. The proximal policy optimization algorithm (PPO) in reinforcement learning has been widely used in the design of autonomous driving decision-making systems due to its stable training process and good performance.Based on the PPO algorithm, this study designed a state space and multi-objective reward function that adapts to different driving scenarios, covering urban roads, highways and human-computer interaction scenarios. A large number of experiments were conducted on the CARLA, SUMO and TORCS simulation platforms to simulate real traffic environments, and the safety, efficiency and driving comfort of the algorithm were systematically evaluated.The experimental results show that the PPO algorithm can achieve high safety in a variety of complex scenarios, with the average vehicle speed on urban roads maintained between 28.4 and 34 km/h, the success rate of overtaking on highways reaching 72.3% to 90.5%, and the number of emergency brakes effectively reduced. Despite this, the algorithm is still unstable under extremely complex traffic conditions, showing the potential for further improvement.