Reinforcement Learning-based Autonomous Driving Path Planning for Smart Connected Vehicles

This paper investigates a reinforcement learning-based autonomous driving path planning method for smart connected vehicles. As an important part of the future transportation system, the development of autonomous driving technology for smart connected vehicles has attracted much attention. Among many path planning methods, reinforcement learning excels because of its ability to learn and optimize strategies in complex and dynamic environments. In this paper, we first introduce the basic principles of reinforcement learning and its current application in autonomous driving, and then design a path planning model suitable for intelligent networked vehicles, describing the problem definition, algorithm selection and improvement, reward function design, and model training and optimization process in detail. The effectiveness of the proposed method is verified through experiments, and the experimental results show that the path planning model based on reinforcement learning is able to realize efficient and safe autonomous driving under complex road conditions. Finally, the paper discusses the application prospects and limitations of the research and looks forward to future research directions.