Dynamic obstacle avoidance path planning method for autonomous driving based on quantum ant colony algorithm

被引：0

作者：

Yao, Y. ^{[1
]}

Wang, A.J. ^{[1
]}

Shang, F.M. ^{[2
]}

机构：

[1] College of Information Engineering, Zhengzhou University of Technology, Zhengzhou

[2] International Education College, Zhengzhou University of Light Industry, Zhengzhou

来源：

Advances in Transportation Studies | 2024年 / 2卷 / Special issue期

关键词：

Autonomous driving; Dynamic obstacle avoidance; Path planning; Quantum ant colony algorithm;

D O I：

10.53136/97912218141253

中图分类号：

学科分类号：

摘要：

Addressing the challenges of extensive obstacle avoidance distances and sluggish response times inherent in conventional obstacle avoidance path planning approaches, a dynamic obstacle avoidance path planning method for autonomous driving based on quantum ant colony algorithm is proposed. Firstly, the free space method is used to construct the environmental model. Then, in the environmental model, the ant colony algorithm is used to calculate the heuristic function between nodes and update pheromones, thus generating the automatic driving path. Finally, the quantum ant colony algorithm is used to select the dynamic obstacle avoidance path among the generated automatic driving paths, so as to realize the dynamic obstacle avoidance path planning of automatic driving. The experiments show that the shortest obstacle avoidance distance of this method is 0.915m and 1,292m shorter than that of the two experimental comparison methods respectively, and the obstacle avoidance response time is always less than 1.0s. © 2024, Aracne Editrice. All rights reserved.

引用

页码：29 / 40

页数：11

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