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

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- optimi...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2021-12, Vol.51 (12), p.9015-9029
Main Authors: Liu, Xiaohuan, Zhang, Degan, Zhang, Ting, Cui, Yuya, Chen, Lu, Liu, Si
Format: Article
Language:English
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
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Summary: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.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-021-02303-8