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Improved Hybrid A-Star Algorithm for Path Planning in Autonomous Parking System Based on Multi-Stage Dynamic Optimization

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Abstract

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.

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Abbreviations

A(s):

neighboring interest area

d 0 :

distance to the nearest obstacle

d V :

distance to the nearest edge of the generalized voronoi diagram

f(x, y):

total cost function at the point (x, y)

g(x, y):

motion cost function at the point (x, y)

H :

height of the neighboring interest area

h(x, y):

heuristic cost function at the point (x, y)

r :

resolution parameter

S :

state set

θ :

heading angle

s(x, y, θ):

state variable at the point (x, y) and with the heading angle of θ

u :

weight constants in the dynamical optimization scheme

v(x, y):

safety cost function at the point (x, y)

W :

width of the neighboring interest area

w :

weight constants of the cost functions in the improved algorithm

α :

constant that controls the falloff rate

πv(x, y):

voronoi field potential

∇:

gradient

Ω(s):

admissible set in the strategy

i :

index

mid:

middle point

max:

maximum value

p :

point on RS path

goal:

target state

popped state:

popped state during the searching

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Acknowledgement

This research is sponsored in part by the NSFC Program (No. 61872217, No. U1701262, No. U1801263). Besides, the research is also sponsored in part by the Guangdong Provincial Key Laboratory of Cyber-Physical Systems, the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body at Hunan University (No. 32115018) and the Industrial Internet innovation and development project of ministry of industry and information technology.

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Authors and Affiliations

  1. School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China

    Tianchuang Meng, Jin Huang, Wei Zhang, Yifan Jia, Diange Yang & Zhihua Zhong

  2. School of Mechanical Engineering, Tongji University, Shanghai, 201804, China

    Tianhong Yang & Wenrui Jin

  3. Jiangxi Kmax Industrial Co., Ltd., No.1589 Zishi Road, Xinjian District, Nanchang, 330100, China

    Keqian Wan & Gang Xiao

  4. Chinese Academy of Engineering, No.2 Bingjiaokou Bystreet, Xicheng District, Beijing, 100088, China

    Zhihua Zhong

  5. The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, Hunan, 410082, China

    Tianhong Yang & Wenrui Jin

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  1. Tianchuang Meng
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  2. Tianhong Yang
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  3. Jin Huang
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Correspondence to Jin Huang.

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Meng, T., Yang, T., Huang, J. et al. Improved Hybrid A-Star Algorithm for Path Planning in Autonomous Parking System Based on Multi-Stage Dynamic Optimization. Int.J Automot. Technol. 24, 459–468 (2023). https://doi.org/10.1007/s12239-023-0038-1

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