考虑复杂地形和障碍尺度的无人车轨迹规划

doi:10.19562/j.chinasae.qcgc.2025.04.006

Abstract

Abstract:

Unstructured road often has uneven surface and varying obstacle sizes. Neglecting the uneven terrain and handling obstacles improperly can lead to an imbalance between vehicle safety and travel efficiency. To cope with this challenge， in this paper a trajectory planning method that considers complex terrain and obstacle scales （TOTP） for unstructured road is proposed. Firstly， the trajectory-planning framework for unstructured road is established based on vehicle passability analysis， to determine the optimal travel pattern. Then， an operational risk field is established based on road roughness and obstacle’s size information. In addition， considering both operational risk and travel efficiency， an obstacle avoidance path planning method based on dynamic programming and an obstacle crossing path planning method based on improved A* are proposed. Furthermore， based on vehicle stability analysis， a speed planning method considering terrain constraints is proposed. Finally， real-world experiments are conducted， and the experimental results show that under unstructured road conditions， the trajectory planning method proposed in this paper increases the average vehicle speed by 15.8%， with the average absolute pitch angle and average absolute roll angle reduced by 68.1% and 73.6% respectively. This method can effectively coordinate the safety and efficiency of vehicle operation， demonstrating good generalization and meeting the requirements of real-time performance.

Key words: unstructured road, trajectory planning, path planning, risk filed, speed planning

Congshuai Guo,Hui Liu,Shida Nie,Yingjie Song,Yujia Xie,Fawang Zhang. Trajectory Planning for Autonomous Vehicles Considering Complex Terrains and Obstacle Scales[J].Automotive Engineering, 2025, 47(4): 645-657.

Figures/Tables 28

参数	数值
车辆长度 L_v/m	4.610
车辆宽度 W/m	1.826
车辆高度 H/m	1.763
车辆质量 m/kg	2 285
轴距 L/m	2.69
轮距 B/m	1.581
质心高度 h_g/m	0.67
最小离地间隙 h/m	0.283
两侧轮胎内缘距离 b/m	1.36
车辆前轴刚度 $k f$ /（N·m^-1）	39 600
车辆后轴刚度 $k r$ /（N·m^-1）	39 600

项目	MAP/rad	MAR/rad	MV/（ $m ? s - 1$ ）
EM Planner	0.074	0.096	3.71
TOTP	0.022	0.026	5.21
对比	-70.3%	-72.9%	40.4%

项目	MAP/rad	MAR/rad	MV/（ $m ? s - 1$ ）
EM Planner	0.047	0.072	5.62
TOTP	0.015	0.019	6.51
对比	-68.1%	-73.6%	15.8%

项目

MAP/rad

MAR/rad

/（ $m ? s - 1$ ）

MALA

/（ $m ? s - 2$ ）

避障

0.006

0.009

5.58

1.65

跨障

0.007

0.009

7.23

0.47

对比

16.6%

29.6%

-71.5%

项目	$J ? l o n ? o b j$	$J ? l a t ? o b j$	$J ? l o n ? s$	J
避障	385.44	145.87	120.40	651.71
跨障	245.49	34.92	122.78	403.19

项目	MAP/rad	MAR/rad	MV/（ $m ? s - 1$ ）	MALA/（ $m ? s - 2$ ）
避障	0.015	0.012	6.19	1.53
跨障	0.041	0.039	5.79	0.45
对比	173.3%	225.0%	-6.4%	-70.6%

项目	$J ? l o n ? o b j$	$J ? l a t ? o b j$	$J ? l o n ? s$	J
避障	355.46	137.87	128.72	622.05
跨障	407.34	29.92	307.44	744.7

References 25

1	HU J， HU Y， LU C， et al. Integrated path planning for unmanned differential steering vehicles in off-road environment with 3D terrains and obstacles ［J］. IEEE Transactions on Intelligent Transportation Systems， 2022， 23（6）： 5562-5572.
2	FAN H， ZHU F， LIU C， et al. Baidu apollo EM motion planner ［J］. ArXiv， 2018， abs/1807.08048.
3	SHI Y， HUANG Y， CHEN Y. Trajectory planning of autonomous trucks for collision avoidance with rollover prevention ［J］. IEEE Transactions on Intelligent Transportation Systems， 2021： 1-10.
4	WERLING M， ZIEGLER J， KAMMEL S， et al. Optimal trajectory generation for dynamic street scenarios in a Frenét Frame［C］. Proceedings of the 2010 IEEE International Conference on Robotics and Automation （ICRA）. IEEE， 2010： 987-993.
5	YANG L， QI J， SONG D， et al. Survey of robot 3D path planning algorithms ［J］. Journal of Control Science and Engineering， 2016， 2016： 1-22.
6	CLAUSSMANN L， REVILLOUD M， GRUYER D， et al. A review of motion planning for highway autonomous driving ［J］. IEEE Transactions on Intelligent Transportation Systems， 2020， 21（5）： 1826-1848.
7	PADEN B， ČáP M， YONG S Z， et al. A survey of motion planning and control techniques for self-driving urban vehicles ［J］. IEEE Transactions on Intelligent Vehicles， 2016， 1（1）： 33-55.
8	吴晓建，廖平伟，雷耀，等. 面向结构化道路的智能驾驶轨迹规划一致性研究［J］. 汽车工程， 2024， 46（3）： 383-395，430.
	WU X J， LIAO P W， LEI Y， et al. Research on consistency of intelligent driving trajectory planning for structured road ［J］. Automotive Engineering， 2024， 46（3）： 383-395，430.
9	HART P E， NILSSON N J， RAPHAEL B. A formal basis for the heuristic determination of minimum cost paths ［J］. IEEE Transactions on Systems Science and Cybernetics， 1968， 4（2）： 100-107.
10	SHANG E， DAI B， NIE Y， et al. A guide-line and key-point based A-star path planning algorithm for autonomous land vehicles ［C］. Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems （ITSC）. IEEE， 2020.
11	ERKE S， BIN D， YIMING N， et al. An improved A-Star based path planning algorithm for autonomous land vehicles ［J］. International Journal of Advanced Robotic Systems， 2020， 17（5）.
12	JIANG J， HAN Z， LI J， et al. Global path planning of UGVs in large-scale off-road environment based on improved A-star algorithm and quadratic programming ［C］. Proceedings of the 2023 IEEE Intelligent Vehicles Symposium （IV）. IEEE， 2023.
13	RASTGOFTAR H， ZHANG B， ATKINS E M. A data-driven approach for autonomous motion planning and control in off-road driving scenarios ［Z］. Ithaca； Cornell University Library， arXiv.org. 2018.
14	TIAN F， ZHOU R， LI Z， et al. Trajectory planning for autonomous mining trucks considering terrain constraints ［J］. IEEE Transactions on Intelligent Vehicles， 2021， 6（4）： 772-786.
15	CAI X， EVERETT M， FINK J， et al. Risk-aware off-road navigation via a learned speed distribution map ［C］. Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems （IROS）. IEEE， 2022： 2931-3937.
16	田洪清，丁峰，郑讯佳，等. 基于势能场虚拟力的智能网联车辆运动规划［J］. 汽车工程， 2021， 43（4）： 518-526.
	TIAN H Q， DING F， ZHENG X J， et al. Motion planning based on virtual force of potential field for intelligent connected vehicles ［J］. Automotive Engineering， 2021， 43（4）： 518-526.
17	CHI W， WANG C， WANG J， et al. Risk-DTRRT-based optimal motion planning algorithm for mobile robots ［J］. IEEE Transactions on Automation Science and Engineering， 2019， 16（3）： 1271-1288.
18	田洪清，王建强，黄荷叶，等. 越野环境下基于势能场模型的智能车概率图路径规划方法［J］. 兵工学报， 2021， 42（7）： 1496-1505.
	TIAN H Q， WANG J Q， HUANG H Y， et al. Probabilistic roadmap method for path planning of intelligent vehicle based on artificial potential field model in off-road environment ［J］. Acta Armamentarii， 2021， 42（7）： 1496-1505.
19	TANG C， ZHAO Y. Hierarchical path planning based on PPO for UVs on 3D off-road terrain ［C］. Proceedings of the 2022 IEEE 4th International Conference on Power， Intelligent Computing and Systems （ICPICS）. IEEE， 2022： 295-300.
20	YANG L， WANG Q， TAN Y， et al. Autonomous vehicle path planning considering dwarf or negative obstacles ［C］. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium （IV）. IEEE， 2019： 1021-1026.
21	LUO H， LI M， LIANG G， et al. An obstacle-crossing strategy based on the fast self-reconfiguration for modular sphere robots［C］. Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems （IROS）. IEEE， 2020： 3296-3303.
22	CHEN M， CAO Y， TIAN Y， et al. A passive compliance obstacle-crossing robot for power line inspection and maintenance ［J］. IEEE Robotics and Automation Letters， 2023， 8（5）： 2772-2779.
23	BERTI M， CORSINI A， DAEHNE A. Comparative analysis of surface roughness algorithms for the identification of active landslides ［J］. Geomorphology， 2013， 182： 1-18.
24	余志生. 汽车理论［M］. 6版. 北京：机械工业出版社， 2018.
	YU Z S. Automotive theory ［M］. 6th ed. Beijing： China Machine Press， 2018.
25	RAJAMANI R. Vehicle dynamics and control ［M］. Springer Science & Business Media， 2011.

工况名称	工况特点	试验目的
工况1	包含起伏路面	验证TOTP对复杂地形的处理能力	算法有效性	泛化能力
工况2	包含起伏路面和不可跨越障碍	验证TOTP对复杂地形和不可跨越障碍物的处理能力	算法有效性
工况3	包含可跨越障碍和不可跨越障碍	验证非结构化道路同时存在可跨越和不可跨越障碍时TOTP的处理能力	决策合理性
工况4	包含可跨越障碍和起伏路面	验证同时存在起伏道路和可跨越障碍条件下TOTP的处理能力	决策合理性

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Trajectory Planning for Autonomous Vehicles Considering Complex Terrains and Obstacle Scales

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