基于改进混合A*的智能汽车时空联合规划方法

doi:10.19562/j.chinasae.qcgc.2023.07.003

汽车工程 ›› 2023, Vol. 45 ›› Issue (7): 1123-1133.doi: 10.19562/j.chinasae.qcgc.2023.07.003

所属专题：智能网联汽车技术专题-规划&决策2023年

• 专题：汽车智能化关键技术 • 上一篇下一篇

基于改进混合A*的智能汽车时空联合规划方法

胡杰^1,^2,³(),张志豪^1,^2,³,陈瑞楠^1,^2,³,陈锐鹏^1,^2,³,刘昊岩^1,^2,³,朱琪^1,^2,³,陈晖⁴

^1.武汉理工大学，现代汽车零部件技术湖北省重点实验室，武汉 430070
^2.武汉理工大学，汽车零部件技术湖北省协同创新中心，武汉 430070
^3.武汉理工大学，新能源与智能网联汽车湖北省工程技术中心，武汉 430070
^4.东风汽车股份有限公司商品研发院，武汉 430000

收稿日期:2023-01-12 修回日期:2023-02-23 出版日期:2023-07-25 发布日期:2023-07-25
通讯作者: 胡杰 E-mail:auto_hj@163. com
基金资助:
湖北省科技重大专项(2020AAA001)

Spatio-temporal Joint Planning Method of Intelligent Vehicles Based on Improved Hybrid A

Jie Hu^1,^2,³(),Zhihao Zhang^1,^2,³,Ruinan Chen^1,^2,³,Ruipeng Chen^1,^2,³,Haoyan Liu^1,^2,³,Qi Zhu^1,^2,³,Hui Chen⁴

^1.Wuhan University of Technology，Hubei Key Laboratory of Modern Auto Parts Technology，Wuhan 430070
^2.Wuhan University of Technology，Auto Parts Technology Hubei Collaborative Innovation Center，Wuhan 430070
^3.Wuhan University of Technology，Hubei Technology Research Center of New Energy and Intelligent Connected Vehicle Engineering，Wuhan 430070
^4.Commercial Product R&D Institute，Dongfeng Automobile Co. ，Ltd. ，Wuhan 430000

Received:2023-01-12 Revised:2023-02-23 Online:2023-07-25 Published:2023-07-25
Contact: Jie Hu E-mail:auto_hj@163. com

摘要/Abstract

摘要：

运动规划是自动驾驶系统生成轨迹的关键模块，现有运动规划多采用路径和速度解耦的方法，但解耦的规划方法在复杂动态场景下易陷入轨迹次优。本文提出了一种基于搜索和数值优化结合的时空联合运动规划方法，直接求解可行驶轨迹。首先使用改进混合A*在时空范围内进行轨迹粗搜索获得初始轨迹，然后以初始轨迹为参考构建可行驶时空走廊，并综合考虑车辆动力学和轨迹连续性约束等条件，使用数值优化的方法进一步平滑初始轨迹。选取换道超车和旁车切入两类典型复杂动态场景进行仿真测试，结果表明，所提时空联合规划方法相较于传统时空解耦规划方法更加灵活、规划结果更加合理，同时具有较好的计算实时性。

关键词: 时空联合规划, 改进混合A*, 时空走廊, 数值优化

Abstract:

Motion planning is the critical module of trajectory generation in autopilot system. The existing motion planning mostly adopts path-velocity decomposition method， which is easy to fall into trajectory suboptimal in complex dynamic scenarios. In this paper， a spatio-temporal joint motion planning method based on the combination of search and numerical optimization is proposed to solve the drivable trajectory directly. Firstly， the improved hybrid A* is used to search for the initial rough trajectory in the spatio-temporal range. Secondly， a drivable spatio-temporal corridor is constructed based on the initial trajectory， and considering vehicle dynamics and trajectory continuity constraints， the numerical optimization method is used to further smooth the initial trajectory. Finally， two typical complex dynamic scenarios of lane change overtaking and side-vehicle cut-in are selected for simulation test. The results show that the proposed planning method is more flexible and more reasonable than the traditional spatio-temporal decoupling planning method， and has better real-time computing performance.

Key words: spatio-temporal joint planning, improved hybrid A*, spatio-temporal corridor, numerical optimization

胡杰, 张志豪, 陈瑞楠, 陈锐鹏, 刘昊岩, 朱琪, 陈晖. 基于改进混合A*的智能汽车时空联合规划方法[J]. 汽车工程, 2023, 45(7): 1123-1133.

Jie Hu, Zhihao Zhang, Ruinan Chen, Ruipeng Chen, Haoyan Liu, Qi Zhu, Hui Chen. Spatio-temporal Joint Planning Method of Intelligent Vehicles Based on Improved Hybrid A[J]. Automotive Engineering, 2023, 45(7): 1123-1133.

图/表 25

图1

图2

图3

表1

时空混合A*算法伪代码"

算法1 时空混合A*算法流程

输入：车辆配置参数，道路环境信息，障碍物信息

输出：初始粗轨迹

1. Initialize the open set and closed set with the $N s t a r t$ ；

2. while the open set is not empty

3. Estimate the nodes’ cost in the open set according to equation （4） and （5） and take the node with the lowest cost as current node；

4. Add the current node to the closed set；

5. if the current node is the destination node $N e n d$

break；

6. else

7. Remove the current node from the open set；

8. Generate the neighboring nodes with the time step （dt） according

to equation（3）；

9. for each neighboring node

10. if it is in the closed set or doesn’t pass the reasonability check

11. continue；

12. else

13. if it is not in the open set， add it to the open set；

14. end

15. end

16. end

17. end

18. Trajectory = Back track nodes in the closed set from the $N e n d$ to $N s t a r t$ by the lowest cost；

19. return coarse trajectory；

表1

图4

图5

图6

表2

仿真实验相关参数"

参数	数值
车长 $L V / m$	4.79
车宽 $W / m$	1.92
轴距 $L / m$	2.91
前轮转角范围 $δ / (°)$	［-22， 22］
速度范围 $v / (m · s - 1)$	［0， 20］
加速度范围 $a / (m · s - 2)$	［-3， 2］
离散加速度集 $A$	｛-3，-1，-0.2， 0， 0.1， 0.3，0.6，1，1.5，2｝
前轮转角离散数量 $N$	11

表2

图7

图8

图9

图10

图11

图12

图13

图14

图15

图16

图17

图18

图19

图20

图21

图22

表3

参考文献 18

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场景	平均耗时/ms
场景	本文方法	Lattice^［18］	时空RRT*^［6］
换道超车	117	57	683
旁车切入	76	35	410

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基于改进混合A*的智能汽车时空联合规划方法

Spatio-temporal Joint Planning Method of Intelligent Vehicles Based on Improved Hybrid A

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