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

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Abstract

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.

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Authors and Affiliations

  1. Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin, 300384, China

    Xiaohuan Liu, Degan Zhang, Ting Zhang, Yuya Cui, Lu Chen & Si Liu

  2. Tianjin Key Lab of Intelligent Computing & Novel software Technology, Tianjin University of Technology, Tianjin, 300384, China

    Xiaohuan Liu, Degan Zhang, Ting Zhang, Yuya Cui, Lu Chen & Si Liu

Authors
  1. Xiaohuan Liu
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  2. Degan Zhang
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  3. Ting Zhang
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  4. Yuya Cui
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  5. Lu Chen
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  6. Si Liu
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Correspondence to Xiaohuan Liu or Degan Zhang.

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Liu, X., Zhang, D., Zhang, T. et al. Novel best path selection approach based on hybrid improved A* algorithm and reinforcement learning. Appl Intell 51, 9015–9029 (2021). https://doi.org/10.1007/s10489-021-02303-8

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