Novel best path selection approach based on hybrid improved A algorithm and reinforcement learning

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 51; no. 12; pp. 9015 - 9029
• Main Authors: Liu, Xiaohuan, Zhang, Degan, Zhang, Ting, Cui, Yuya, Chen, Lu, Liu, Si
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computer Science; Driving; Emergency plans; Hybrid systems; Machine learning; Machines; Manufacturing; Mechanical Engineering; Path planning; Processes; Traffic congestion; Traffic jams; Vehicles; A algorithm; Path planning; Intelligent driving; Reinforcement learning
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-021-02303-8

Path planning of intelligent driving vehicles in emergencies is a hot research issue, this paper proposes a new method of the best path selection for the intelligent driving vehicles to solve this problem. Based on the prior knowledge applied reinforcement learning strategy and the searching- optimized A* algorithm, we designed a hybrid algorithm to help intelligent driving vehicles selecting the best path in the traffic network in emergencies including limited height, width, 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.