Research on Path Planning Algorithm Based on the Integration of Ant Colony Algorithm and AI Decision

How to plan the vehicle path efficiently and accurately is a key problem in route planning. To solve this problem, in this paper, an Algorithm combining Ant Colony Algorithm (ACA) and Proximal Policy Optimization (Proximal Policy Optimization) is proposed, PPO's new approach-Optimization-Ant Colony Policy (OACP). OACP method realizes the preliminary exploration and optimization of the path through ACA, and uses the pheromone left by ants in the search process to guide the path selection. At the same time, PPO is used for strategy learning and dynamic decision-making, and the path planning strategy is constantly adjusted through reinforcement learning mechanism, so as to ensure high planning accuracy and improve the real-time adaptability and robustness of the path. Finally, through the comparison experiment and analysis with the traditional path planning algorithm learning, the experimental results show that O-ACP method has significant advantages in improving the precision of path planning and the number of iterations. Especially in the complex road environment, O-ACP method can more accurately plan the optimal path and reduce the uncertainty in the decision-making process. This fully verifies the reliability and effectiveness of the architecture in intelligent driving path planning.