基于层次迭代搜索的自动驾驶重载半挂牵引车轨迹规划方法

doi:10.3901/JME.2025.10.335

机械工程学报 ›› 2025, Vol. 61 ›› Issue (10): 335-348.doi: 10.3901/JME.2025.10.335

• 运载工程 • 上一篇

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基于层次迭代搜索的自动驾驶重载半挂牵引车轨迹规划方法

贺宜^1,2,3, 杜宇豪^1,2,3, 吴钪⁴, 邱志军^1,2,3

1. 武汉理工大学智能交通系统研究中心武汉 430063;
2. 交通信息与安全教育部工程研究中心武汉 430063;
3. 车路一体智能交通全国重点实验室北京 100088;
4. 山河智能装备股份有限公司长沙 410100

收稿日期:2024-05-22 修回日期:2024-12-02 发布日期:2025-07-12
作者简介:贺宜，男，1986年出生，博士，研究员，博士研究生导师。主要研究方向为车辆动力学建模与控制、智能驾驶与自动驾驶。E-mail：heyi@whut.edu.cn;杜宇豪(通信作者)，男，1999年出生。主要研究方向为车辆运动规划。E-mail：yuhao_du@whut.edu.cn
基金资助:
车路一体智能交通全国重点实验室开放基金课题(2024-A003)和国家自然科学基金(52322217)资助项目。

Hierarchical Iterative Search-based Trajectory Planning Method for Self-driving Heavy-duty Semi-trailer Tractors

He Yi^1,2,3, DU Yuhao^1,2,3, Wu Kang⁴, Qiu Zhijun^1,2,3

1. Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063;
2. Engineering Research Center for Traffic Information and Safety, Ministry of Education, Wuhan 430063;
3. National Key Laboratory of Vehicle Road Integrated Intelligent Transportation, Beijing 100088;
4. Sunward Intelligent Equipment Co., Ltd., Changsha 410100

Received:2024-05-22 Revised:2024-12-02 Published:2025-07-12

摘要/Abstract

摘要： 针对半挂牵引车运动学模型与安全约束的高度非线性导致动态轨迹求解效率缓慢的问题，提出一种层次迭代搜索轨迹规划方法。首先，基于Frenet坐标系将半挂牵引车的横纵向运动解耦，设计基于半挂牵引车运动学的多项式轨迹采样求解方法，分别获取牵引车和拖挂车的运动轨迹。其次，为简化搜索过程，提升轨迹求解速度，构建先验损失估计模型并利用层次迭代搜索框架获得最优候选轨迹集合。最后，通过曲率、加速度和多圆包络碰撞检查选择最优可行运动轨迹。仿真表明，所提算法能够生成平滑且无碰撞的半挂牵引车运动轨迹，在复杂工况下完成避障任务，相比两种现有算法，求解效率分别提升85%和58%，能更好地平衡轨迹质量和求解效率之间的关系，满足实时避障路径求解的需要。

关键词: 汽车工程, 半挂牵引车, 层次迭代搜索, 轨迹规划, 自动驾驶

Abstract: A hierarchical iterative search trajectory planning method is proposed to address the slow efficiency of dynamic trajectory solving caused by the highly nonlinear kinematic model and safety constraints of semi-trailer tractors. Firstly, based on the Frenet coordinate system, the lateral and longitudinal motion of the semi-trailer tractor is decoupled, and a polynomial trajectory sampling solution method based on the kinematics of the semi-trailer tractor is designed to obtain the motion trajectories of the tractor and trailer separately. Secondly, to simplify the search process and improve trajectory solving speed, a prior loss estimation model is constructed and a hierarchical iterative search framework is used to obtain the optimal candidate trajectory. Finally, the optimal feasible motion trajectory is selected through curvature, acceleration, and multi circle envelope collision checks. Simulation results show that the proposed algorithm can generate smooth and collision free motion trajectories for semi-trailer tractors, and complete obstacle avoidance tasks under complex working conditions. Compared with the two existing algorithms, the solving efficiency is improved by 85% and 58% respectively, which can better balance the relationship between trajectory quality and solving efficiency and meet the needs of real-time obstacle avoidance path solving.

Key words: automotive engineering, semi trailer tractor, hierarchical iterative search, trajectory planning, autonomous driving

中图分类号:

U463

贺宜, 杜宇豪, 吴钪, 邱志军. 基于层次迭代搜索的自动驾驶重载半挂牵引车轨迹规划方法[J]. 机械工程学报, 2025, 61(10): 335-348.

He Yi, DU Yuhao, Wu Kang, Qiu Zhijun. Hierarchical Iterative Search-based Trajectory Planning Method for Self-driving Heavy-duty Semi-trailer Tractors[J]. Journal of Mechanical Engineering, 2025, 61(10): 335-348.

参考文献

[1] 中华人民共和国国家统计局. 中国统计年鉴[M]. 北京：中国统计出版社，2023. National Bureau of Statistics of China. China statistical yearbook[M]. Beijing：China Statistical Publishing House，2023.
[2] Hamido S，Hamamoto R，Gu Xinzhu，et al. Factors influencing occupational truck driver safety in ageing society[J]. Accident Analysis & Prevention，2021，150：105922.
[3] 严新平，褚端峰，刘佳仑，等. 智能交通发展的现状、挑战与展望[J]. 交通运输研究，2021，7(6)：2-10，22. Yan Xinping，Chu Duanfeng，Liu Jialun，et al. The current status，challenges，and prospects of intelligent transportation development[J]. Transportation Research，2021，7(6)：2-10，22.
[4] 张龙龙，王学伟，王锋. 无人卡车的发展现状与应用前景[J]. 时代汽车，2023(5)：16-18. ZHANG Longlong，WANG Xuewei，WANG Feng. Development status and application prospects of unmanned trucks[J]. Auto Time，2023(5)：16-18.
[5] 高林，吴青，贺宜. 复杂工况下货运车辆质量和道路坡度联合估计[J]. 机械工程学报，2025，61(2)：281-295. GAO Lin，WU Qing，HE Yi. Joint estimation of freight vehicle mass and road slope under complex working conditions[J]. Journal of Mechanical Engineering，2025，61(2)：281-295.
[6] TSAI C Y. Trajectory planning of semi-trailer truck vehicle based on algebraic general trajectory formula[J]. IEEE Transactions on Intelligent Transportation Systems，2022，24(2)：1495-1501.
[7] TENG Siyu，HU Xuemin，DENG Peng，et al. Motion planning for autonomous driving：The state of the art and future perspectives[J]. IEEE Transactions on Intelligent Vehicles，2023，8(6)：3692-3711.
[8] LI Bai，ACARMAN T，ZHANG Youmin，et al. Tractor-trailer vehicle trajectory planning in narrow environments with a progressively constrained optimal control approach[J]. IEEE Transactions on Intelligent Vehicles，2019，5(3)：414-425.
[9] 张智能，李以农，余颖弘，等. 复杂动态环境下智能汽车局部路径规划与跟踪算法研究[J]. 中国公路学报，2022，35(9)：372-386. ZHANG Zhineng，LI Yinong，YU Yinghong，et al. Research on local path planning and tracking algorithms for intelligent vehicles in complex dynamic environments[J]. Journal of China Highway Engineering，2022，35(9)：372-386.
[10] Dolgov D，Thrun S，Montemerlo M，et al. Practical search techniques in path planning for autonomous driving[J]. Ann Arbor，2008，1001(48105)：18-80.
[11] Beyersdorfer S，Wagner S. Novel model based path planning for multi-axle steered heavy load vehicles[C]//16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013). New York：IEEE，2013：424-429.
[12] Rimmer A J，Cebon D. Planning collision-free trajectories for reversing multiply-articulated vehicles[J]. IEEE Transactions on Intelligent Transportation Systems，2016，17(7)：1998-2007.
[13] Wang Zejiang，Ahmad A，Quirynen R，et al. Motion planning and model predictive control for automated tractor-trailer hitching maneuver[C]//2022 IEEE Conference on Control Technology and Applications (CCTA). New York：IEEE，2022：676-682.
[14] González D，Pérez J，Milanés V，et al. A review of motion planning techniques for automated vehicles[J]. IEEE Transactions on Intelligent Transportation Systems，2015，17(4)：1135-1145.
[15] Bergman K，Ljungqvist O，Axehill D. Improved path planning by tightly combining lattice-based path planning and optimal control[J]. IEEE Transactions on Intelligent Vehicles，2020，6(1)：57-66.
[16] Ruan Xinyao，Huang Yanjun，Wang Yulei，et al. An efficient trajectory planning method with a reconfigurable model for any tractor-trailer vehicle[J]. IEEE Transactions on Transportation Electrification，2022，9(2)：3360-3374.
[17] Liu Jie，Dong Xianzhou，Wang Junyu，et al. A novel EPT autonomous motion control framework for an off-axle hitching tractor-trailer system with drawbar[J]. IEEE Transactions on Intelligent Vehicles，2020，6(2)：376-385.
[18] Cen Hangjie，Li Bai，ACARMAN T，et al. Optimization-based maneuver planning for a tractor-trailer vehicle in complex environments using Safe travel corridors[C]//2021 IEEE Intelligent Vehicles Symposium (IV). New York：IEEE，2021：974-979.
[19] Bergman K，Axehill D. Combining homotopy methods and numerical optimal control to solve motion planning problems[C]//2018 IEEE Intelligent Vehicles Symposium (IV). New York：IEEE，2018：347-354.
[20] 魏民祥，滕德成，吴树凡. 基于Frenet坐标系的自动驾驶轨迹规划与优化算法[J]. 控制与决策，2021，36(4)：815-824. Wei Minxiang，Teng Decheng，Wu Shufan. Autonomous driving trajectory planning and optimization algorithm based on Frenet coordinate system[J]. Control and Decision-making，2021，36(4)：815-824.
[21] 王明强，王震坡，张雷. 基于碰撞风险评估的智能汽车局部路径规划方法研究[J]. 机械工程学报，2021，57(10)：28-41. WANG Mingqiang，WANG Zhenpo，ZHANG Lei. Research on local path planning method for intelligent vehicles based on collision risk assessment[J]. Journal of Mechanical Engineering，2021，57(10)：28-41.
[22] Evestedt N，Ljungqvist O，Axehill D. Motion planning for a reversing general 2-trailer configuration using closed-loop rrt[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York：IEEE，2016：3690-3697.
[23] Ljungqvist O，Evestedt N，Cirillo M，et al. Lattice-based motion planning for a general 2-trailer system[C]//2017 IEEE Intelligent Vehicles Symposium (IV). New York：IEEE，2017：819-824.
[24] Shen Qiyue，Wang Bing，Wang Chunxiang. Real-time trajectory planning for on-road autonomous tractor-trailer vehicles[J]. Journal of Shanghai Jiaotong University (Science)，2021，26：722-730.
[25] Werling M，Ziegler J，Kammel S，et al. Optimal trajectory generation for dynamic street scenarios in a frenet frame[C]//2010 IEEE International Conference on Robotics and Automation. New York：IEEE，2010：987-993.
[26] Peng Bo，Yu Dexin，Zhou Huxing，et al. A motion planning method for automated vehicles in dynamic traffic scenarios[J]. Symmetry，2022，14(2)：208.
[27] SUN Shuo，LIU Zhiyang，YIN Huan，et al. FISS：A trajectory planning framework using fast iterative search and sampling strategy for autonomous driving[J]. IEEE Robotics and Automation Letters，2022，7(4)：9985-9992.

基于层次迭代搜索的自动驾驶重载半挂牵引车轨迹规划方法

Hierarchical Iterative Search-based Trajectory Planning Method for Self-driving Heavy-duty Semi-trailer Tractors

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