| IMPROVED HYBRID A-STAR ALGORITHM FOR PATH PLANNING IN
AUTONOMOUS PARKING SYSTEM BASED ON MULTI-STAGE
DYNAMIC OPTIMIZATION |
| Tianchuang Meng 1, Tianhong Yang 2,5, Jin Huang 1, Wenrui Jin 2,5, Wei Zhang 1, Yifan Jia 1, Keqian Wan 3, Gang Xiao 3, Diange Yang 1, Zhihua Zhong 1,4 |
1School of Vehicle and Mobility, Tsinghua University
2School of Mechanical Engineering, Tongji University
3Jiangxi Kmax Industrial Co., Ltd.
4Chinese Academy of Engineering
5The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University |
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| ABSTRACT |
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The recent proliferation of intelligent technologies has promoted autonomous driving. The autonomous
parking system has become a popular feature in autonomous driving. Hybrid A-star algorithm is a commonly used path
planning algorithm for its simplicity to deploy and the good characteristics of the generated paths in the practical engineering.
To further enhance the path safety and efficiency of path planning in the autonomous parking system, this paper proposes an
improved hybrid A-star algorithm through the safety-enhanced design and the efficiency-enhanced design. The
safety-enhanced design integrates the Voronoi field potential into the path searching stage to take more account of path safety.
The efficiency-enhanced design proposes a multi-stage dynamic optimization strategy which divides the path planning into
multiple stages and performs dynamic optimization in each stage. Through simulation experiments, it is verified that the
proposed improved algorithm not only generates a much safer path which stays farther from the obstacles but also
significantly improves the searching efficiency in terms of time and space, merely at a finite cost of pre-processing work
which can also be repeatedly utilized. We hope this paper will promote relative research on path planning in autonomous
parking and serve as a reference for the practical engineering. |
| Key Words:
Autonomous driving, Autonomous parking system, Path planning, Hybrid A-star algorithm, Multi-stage
dynamic optimization, Voronoi field |
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Corresponding Author.
Jin Huang , Email. 
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