Machine Learning-Driven Path Planning and Obstacle Avoidance Algorithms for Autonomous Vehicles

With the rapid advancement of autonomous driving technology, path planning and obstacle avoidance strategies based on machine learning are of great significance for the efficient and safe operation of autonomous vehicles. In order to achieve automatic driving path planning and obstacle avoidance, this paper combines deep reinforcement learning and probability graph model to propose a complex method. First, Deep Q Network (DQN) perceives and decides the surrounding environment to obtain real-time information about road conditions, obstacle locations and other traffic participants. Markov Decision Process (MDP) etc are introduced to further perform path planning and dynamic obstacle prediction, optimize the trajectory selection and obstacle avoidance strategy of vehicles. The experimental results demonstrate that the proposed method achieves a much superior performance in the complex urban road environment, which can significantly enhance the operating precision and traffic safety of an autonomous vehicle. The suggested approach has validated terrestrial experiments in root tests, providing tremendous enhancements in navigation accuracy and measures of traffic safety for autonomous vehicles.