New Method of the Best Path Selection with Length Priority Based on Reinforcement Learning Strategy

摘要

This paper proposes and designs a new method of the best path selection algorithm with length priority to analyze and solve the optimal path planning problem of intelligent driving vehicles in practical applications. Through the understanding and learning of the reinforcement learning algorithm, we proposed a new method of the best path selection with length priority based on the prior knowledge applied reinforcement learning strategy, and improved the search direction setting of the shortest path in the program, simplified the process of shortest path search. This path optimization method can effectively help different types of intelligent driving vehicles to smoothly select the best path in the traffic network with limited height, width and weight, accident and traffic jam. Through simulation experiments and scene experiments, it is proved that the proposed algorithm has good stability, high efficiency and practicability.