路端强化的自动驾驶非结构化道路运动规划：以ETC收费站为例

雷明月; 赖金涛; 胡笳

doi:10.19818/j.cnki.1671-1637.2026.078

路端强化的自动驾驶非结构化道路运动规划：以ETC收费站为例

doi: 10.19818/j.cnki.1671-1637.2026.078

雷明月^1,,
赖金涛²,
胡笳^1, ,

1.
同济大学道路与交通工程教育部重点实验室, 上海 201804
2.
同济大学电子与信息工程学院, 上海 201804

基金项目:

国家重点研发计划 2022YFE0117100

国家自然科学基金项目 52502406

国家自然科学基金项目 52372317

长三角科技创新共同体联合攻关项目 2023CSJGG0800

上海汽车工业科技发展基金 2404

中央高校基本科研业务费专项资金项目 22120230311

中特智能讲席教授基金 000000375-2018082

湖南大学整车先进设计制造技术全国重点实验室开放基金 32215011

国家资助博士后研究人员计划 GZB20240541

上海市科技计划项目 25ZR1402500

详细信息

作者简介:
雷明月(1998-)，女，安徽芜湖人，工学博士，E-mail: 2210177@tongji.edu.cn

通讯作者:
胡笳(1988-)，男，浙江杭州人，教授，博士生导师，工学博士，E-mail: hujia@tongji.edu.cn

中图分类号: U461.91
计量
- 文章访问数: 11
- HTML全文浏览量: 8
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2025-03-20
- 录用日期: 2025-09-26
- 修回日期: 2025-08-27
- 刊出日期: 2026-06-28

Infrastructure enhanced motion planning for automatic driving on unstructured roads: A case study of ETC stations

LEI Ming-yue^1
,,
LAI Jin-tao²,
HU Jia^{1
, ,}

1.
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
2.
College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China

Funds:

National Key R&D Program of China 2022YFE0117100

National Natural Science Foundation of China 52502406

National Natural Science Foundation of China 52372317

Yangtze River Delta Science and Technology Innovation Joint Force Project 2023CSJGG0800

Shanghai Automotive Industry Science and Technology Development Foundation 2404

Fundamental Research Funds for the Central Universities 22120230311

Tongji Zhongte Chair Professor Foundation 000000375-2018082

Science Fund of State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle 32215011

Postdoctoral Fellowship Program of CPSF GZB20240541

Shanghai Science and Technology Plan Project 25ZR1402500

More Information

Corresponding author: HU Jia, professor, PhD, E-mail: hujia@tongji.edu.cn

Article Text (Baidu Translation)

摘要

摘要: 为提升基于优化控制的网联自动驾驶车辆运动规划方法的安全性、普适性、实用性，提出了一种路端强化的网联自动驾驶车辆运动规划方法；构建基于控制障碍函数的运动控制系统，保证非线性系统构造下的安全避障强约束，实现了车辆提前响应，避让超出可视范围的障碍物；提出以优化路径为参考线的坐标系，消除了对道路中心线的依赖，适配非结构化道路环境下的运动规划；提出了一种“路助车”式运动规划方法，打破了传统“路开车”式运动规划方法计算效率低、安全性低、实用性差的局限；以ETC收费站场景为典型案例，对所提出的运动规划方法进行了仿真测试。研究结果表明：在存在车道线瞬变、车道线消失、占道施工的典型非结构化道路场景上，所提出的方法表现优于传统车端运动规划模型，行车风险降低了11.34%，通行效率提高了17.36%，各模块平均单次响应运行时长均小于0.05 s。所提出的运动规划方法能有效降低超视距风险，提高通过非结构化道路效率，提升算法应用实时性，并能保证在不同车路通信条件下的鲁棒性。
- 汽车工程 /
- 网联自动驾驶车辆 /
- 运动规划 /
- 优化控制 /
- 路端强化 /
- 控制障碍函数
Abstract: To enhance the safety, generality, and practicality of the motion planning method for connected and automated vehicles based on optimal control, an infrastructure enhanced motion planning method was proposed for connected and automated vehicles. A motion control system based on a control barrier function was constructed to ensure strong constraints for safe collision avoidance under nonlinear system configuration, enabling vehicles to respond proactively and avoid over-the-horizon obstacles. A coordinate system with the optimized path as the reference line was proposed. It eliminated the dependence on road centerlines, and adapted to motion planning in unstructured road environments. An "infrastructure assisting vehicle" motion planning method was proposed, breaking the limitations of low computational efficiency, low safety, and poor practicality inherent in the traditional "infrastructure driving vehicle" motion planning method. Simulation testing was conducted on the proposed motion planning method, with electronic toll collection (ETC) stations taken as a typical case. The research results show that on typical unstructured road scenarios characterized by transient lane markings, missing lane markings, and lane-occupying construction, the proposed method is superior to traditional onboard motion planning models. The driving risk is reduced by 11.34%, and travel efficiency is increased by 17.36%. The average single-response runtime is less than 0.05 s for each module. The proposed motion planning method effectively reduces over-the-horizon risks, improves the efficiency of traversing unstructured roads, enhances real-time performance in algorithm application, and maintains robustness under varying vehicle-infrastructure communication conditions.
- automotive engineering /
- CAV /
- motion planning /
- optimal control /
- infrastructure enhancement /
- control barrier function

HTML全文

图 1 研究场景

Figure 1. Research scenario

下载: 全尺寸图片幻灯片

图 2 路端强化的网联自动驾驶车辆运动规划架构

Figure 2. Framework of infrastructure enhanced motion planning for connected and automated vehicles

下载: 全尺寸图片幻灯片

图 3 局部运动规划坐标系：以规划路径为参考线

Figure 3. Coordinate system of local motion planning: using planned path as the reference line

下载: 全尺寸图片幻灯片

图 4 试验场景

Figure 4. Testing scenario

下载: 全尺寸图片幻灯片

图 5 平均危险减速度指标

Figure 5. Mean risk deceleration indicator

下载: 全尺寸图片幻灯片

图 6 车辆轨迹

Figure 6. Vehicle trajectories

下载: 全尺寸图片幻灯片

图 7 行程时间指标

Figure 7. Travel time indicator

下载: 全尺寸图片幻灯片

图 8 路端强化运动规划方法在不同非结构化道路类型上的提升效果

Figure 8. Improvement effects of infrastructure enhanced motion planning method for diverse unstructured road types

下载: 全尺寸图片幻灯片

图 9 路端模块与车载模块计算时间对比

Figure 9. Comparison of computation time between infrastructure module and onboard module

下载: 全尺寸图片幻灯片

图 10 通信丢包环境下平均加加速度指标

Figure 10. Average jerk indicator in communication packet loss scenarios

下载: 全尺寸图片幻灯片

图 11 通信时延环境下平均加加速度指标

Figure 11. Average jerk indicator in communication delay scenarios

下载: 全尺寸图片幻灯片

图 12 道路边界不确定环境下车辆轨迹

Figure 12. Vehicle trajectory in uncertain road boundary scenarios

下载: 全尺寸图片幻灯片

参考文献(48)

[1]	杨晓光, 赖金涛, 张振, 等. 车路协同环境下的轨迹级交通控制研究综述[J]. 中国公路学报, 2023, 36(9): 225-243. YANG Xiao-guang, LAI Jin-tao, ZHANG Zhen, et al. Review of trajectory based traffic control in a vehicle-infrastructure cooperative environment[J]. China Journal of Highway and Transport, 2023, 36(9): 225-243.
[2]	王润民, 张心睿, 赵祥模, 等. 混行环境下网联信号交叉口车路协同控制方法[J]. 交通运输工程学报, 2022, 22(3): 139-151. doi: 10.19818/j.cnki.1671-1637.2022.03.011 WANG Run-min, ZHANG Xin-rui, ZHAO Xiang-mo, et al. Vehicle-infrastructure cooperative control method of connected and signalized intersection in mixed traffic environment[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 139-151. doi: 10.19818/j.cnki.1671-1637.2022.03.011
[3]	李立, 徐志刚, 赵祥模, 等. 智能网联汽车运动规划方法研究综述[J]. 中国公路学报, 2019, 32(6): 20-33. LI Li, XU Zhi-gang, ZHAO Xiang-mo, et al. Review of motion planning methods of intelligent connected vehicles[J]. China Journal of Highway and Transport, 2019, 32(6): 20-33.
[4]	ZHANG Z H, LIU H B, LEI M Y, et al. Review on the impacts of cooperative automated driving on transportation and environment[J]. Transportation Research Part D: Transport and Environment, 2023, 115: 103607. doi: 10.1016/j.trd.2023.103607
[5]	WANG F Y, HU J, LAI J T. Testing intelligence: Accelerating the verification and validation of intelligent vehicles[J]. IEEE Transactions on Intelligent Vehicles, 2023, 8(2): 1003-1016. doi: 10.1109/TIV.2023.3246701
[6]	WANG H, WANG X, LI X, et al. A pathway forward: The evolution of intelligent vehicles research on IEEE T-Ⅳ[J]. IEEE Transactions on Intelligent Vehicles, 2022, 7(4): 918-928. doi: 10.1109/TIV.2022.3215784
[7]	LAI J T, ZHANG Z, AN L H, et al. A multi-centralized strategy for trajectory based active traffic management with cooperative automation[J]. IEEE Transactions on Intelligent Transportation Systems, 2025, 26(12): 23021-23040. doi: 10.1109/TITS.2025.3614981
[8]	ZHANG C Z, HUANG W F, NIU T, et al. Review of clustering technology and its application in coordinating vehicle subsystems[J]. Automotive Innovation, 2023, 6(1): 89-115.
[9]	WANG H R, FENG Y W, TIAN Y L, et al. Towards the next level of vehicle automation through cooperative driving: A roadmap from planning and control perspective[J]. IEEE Transactions on Intelligent Vehicles, 2024, 9(3): 4335-4347. doi: 10.1109/TIV.2024.3363873
[10]	HU J, LI S Y, WANG H R, et al. Eco-approach at an isolated actuated signalized intersection: Aware of the passing time window[J]. Journal of Cleaner Production, 2024, 435: 140493. doi: 10.1016/j.jclepro.2023.140493
[11]	胡笳, 罗书源, 赖金涛, 等. 自动驾驶对交通运输系统规划的影响综述[J]. 交通运输系统工程与信息, 2021, 21(5): 52-65, 76. HU Jia, LUO Shu-yuan, LAI Jin-tao, et al. A review of the impact of autonomous driving on transportation planning[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 52-65, 76.
[12]	杨晓光, 朱际宸, 彭晴, 等. 面向预约出行的车路联网与协同交通控制: 前沿与展望[J]. 上海理工大学学报, 2023, 45(4): 307-320, 331. YANG Xiao-guang, ZHU Ji-chen, PENG Qing, et al. Research on vehicle-infrastructure cooperative traffic control for urban road travel reservation: frontiers and prospects[J]. Journal of University of Shanghai for Science and Technology, 2023, 45(4): 307-320, 331.
[13]	LAI J T, HU J, CHEN Z, et al. A generic simulation platform for cooperative adaptive cruise control under partially connected and automated environment[J]. Transportation Research Part C: Emerging Technologies, 2020, 121: 102874. doi: 10.1016/j.trc.2020.102874
[14]	HU J, YAN X R, WANG G, et al. A simulation platform for truck platooning evaluation in an interactive traffic environment[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(10): 13785-13804. doi: 10.1109/TITS.2024.3388161
[15]	LI D F, LIU A, PAN H, et al. Safe, efficient and socially-compatible decision of automated vehicles: A case study of unsignalized intersection driving[J]. Automotive Innovation, 2023, 6(2): 281-296. doi: 10.1007/s42154-023-00219-2
[16]	WANG X Y, LI X J, GUAN Y, et al. Bidirectional potential guided RRT* for motion planning[J]. IEEE Access, 2019, 7: 95046-95057. doi: 10.1109/ACCESS.2019.2928846
[17]	FEIZOLLAHI A, MAYORGA R V. Optimized motion planning of manipulators in partially-known environment using modified D^* Lite algorithm[J]. WSEAS Transactions on System, 2017, 16: 69-75.
[18]	朱冰, 贾士政, 赵健, 等. 自动驾驶车辆决策与规划研究综述[J]. 中国公路学报, 2024, 37(1): 215-240. ZHU Bing, JIA Shi-zheng, ZHAO Jian, et al. Review of research on decision-making and planning for automated vehicles[J]. China Journal of Highway and Transport, 2024, 37(1): 215-240.
[19]	TALAMINO J P, SANFELIU A. Anticipatory kinodynamic motion planner for computing the best path and velocity trajectory in autonomous driving[J]. Robotics and Autonomous Systems, 2019, 114: 93-105. doi: 10.1016/j.robot.2018.11.022
[20]	ARADI S. Survey of deep reinforcement learning for motion planning of autonomous vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(2): 740-759. doi: 10.1109/TITS.2020.3024655
[21]	GUO H Y, KEYVAN-EKBATANI M, XIE K. Modeling coupled driving behavior during lane change: A multi-agent Transformer reinforcement learning approach[J]. Transportation Research Part C: Emerging Technologies, 2024, 165: 104703. doi: 10.1016/j.trc.2024.104703
[22]	LI G F, YANG Y F, LI S, et al. Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness[J]. Transportation Research Part C: Emerging Technologies, 2022, 134: 103452. doi: 10.1016/j.trc.2021.103452
[23]	NAEEM H M Y, BUTT Y A, AHMED Q, et al. Optimal-control-based eco-driving solution for connected battery electric vehicle on a signalized route[J]. Automotive Innovation, 2023, 6(4): 586-596. doi: 10.1007/s42154-023-00255-y
[24]	HU J, LEI M Y, WANG H R, et al. Lane-level navigation based eco-approach[J]. IEEE Transactions on Intelligent Vehicles, 2023, 8(4): 2786-2796. doi: 10.1109/TIV.2023.3239386
[25]	WANG H R, HU J, FENG Y W, et al. Optimal control-based highway pilot motion planner with stochastic traffic consideration[J]. IEEE Intelligent Transportation Systems Magazine, 2023, 15(1): 421-436. doi: 10.1109/MITS.2022.3181172
[26]	WU P, GAO F, TANG X L, et al. An integrated decision and motion planning framework for automated driving on highway[J]. IEEE Transactions on Vehicular Technology, 2023, 72(12): 15574-15584. doi: 10.1109/TVT.2023.3293833
[27]	DEPTULA P, CHEN H Y, LICITRA R A, et al. Approximate optimal motion planning to avoid unknown moving avoidance regions[J]. IEEE Transactions on Robotics, 2020, 36(2): 414-430. doi: 10.1109/TRO.2019.2955321
[28]	WANG H, HUANG Y J, KHAJEPOUR A, et al. Crash mitigation in motion planning for autonomous vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(9): 3313-3323. doi: 10.1109/TITS.2018.2873921
[29]	LI S S, LI Z, YU Z X, et al. Dynamic trajectory planning and tracking for autonomous vehicle with obstacle avoidance based on model predictive control[J]. IEEE Access, 2019, 7: 132074-132086. doi: 10.1109/ACCESS.2019.2940758
[30]	HU J, ZHANG Z H, FENG Y W, et al. Transit signal priority enabling connected and automated buses to cut through traffic[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(7): 8782-8792. doi: 10.1109/TITS.2021.3086110
[31]	胡笳, 王浩然, 冯永威, 等. 基于混合域优化控制的智能网联车辆运动规划模型[J]. 中国公路学报, 2022, 35(3): 43-54. HU Jia, WANG Hao-ran, FENG Yong-wei, et al. An optimal control based motion planner in mixed-domain[J]. China Journal of Highway and Transport, 2022, 35(3): 43-54.
[32]	GRAY A, ABBENA E, SALAMON S. Modern differential geometry of curves and surfaces with Mathematica[J]. CRC Press, 1997. DOI: 10.1051/animres:19960408.
[33]	HANSON A J, MA H. Parallel transport approach to curve framing[R]. Bloomington: Indiana University, Techreports-TR425, 1995: 3-7.
[34]	CHEN J, ZHAN W, TOMIZUKA M. Autonomous driving motion planning with constrained iterative LQR[J]. IEEE Transactions on Intelligent Vehicles, 2019, 4(2): 244-254. doi: 10.1109/TIV.2019.2904385
[35]	COWLAGI R V, TSIOTRAS P. Hierarchical motion planning with kinodynamic feasibility guarantees: Local trajectory planning via model predictive control[C]//IEEE. 2012 IEEE International Conference on Robotics and Automation. New York: IEEE, 2012: 4003-4008.
[36]	HU J, ZHANG Z H, XIONG L, et al. Cut through traffic to catch green light: Eco approach with overtaking capability[J]. Transportation Research Part C: Emerging Technologies, 2021, 123: 102927. doi: 10.1016/j.trc.2020.102927
[37]	CHEN S K, ZONG S Y, CHEN T T, et al. A taxonomy for autonomous vehicles considering ambient road infrastructure[J]. Sustainability, 2023, 15(14): 11258. doi: 10.3390/su151411258
[38]	DU R J, CHEN S K, LI Y J, et al. A framework for lane-change maneuvers of connected autonomous vehicles in a mixed-traffic environment[J]. Electronics, 2022, 11(9): 1350. doi: 10.3390/electronics11091350
[39]	LEI Y Z, CHENG Y, YANG X T. An optimization-free approximation framework for connected and automated vehicles eco-trajectory planning under limited computing capacity[J]. Transportation Research Part C: Emerging Technologies, 2025, 171: 104949. doi: 10.1016/j.trc.2024.104949
[40]	MIHALJ T, LI H X, BABIĆ D, et al. Road infrastructure challenges faced by automated driving: A review[J]. Applied Sciences, 2022, 12(7): 3477. doi: 10.3390/app12073477
[41]	WANG Q, TIAN D X, DUAN X T, et al. Efficient and energy-saving cooperative motion planning for multiple connected and autonomous vehicles at unsignalized intersections[J]. IEEE Transactions on Intelligent Transportation Systems, 2025, 26(6): 7921-7938. doi: 10.1109/TITS.2025.3559186
[42]	ZOU Y G, ZHENG F F, LIU C, et al. Integrated optimization of traffic signals and vehicle trajectories for mixed traffic at signalized intersections: A two-level hierarchical control framework[J]. Transportation Research Part C: Emerging Technologies, 2024, 169: 104884. doi: 10.1016/j.trc.2024.104884
[43]	TAKAHASHI A, HONGO T, NINOMIYA Y, et al. Local path planning and motion control for AGV in positioning[C]// IEEE. Proceedings of IEEE/RSJ International Workshop on Intelligent Robots and Systems. New York: IEEE, 1989: 392-397.
[44]	CHEN R N, HU J, XU W C. An RRT-Dijkstra-based path planning strategy for autonomous vehicles[J]. Applied Sciences, 2022, 12(23): 11982. doi: 10.3390/app122311982
[45]	ZHANG Y, CHEN H Y, WASLANDER S L, et al. Hybrid trajectory planning for autonomous driving in highly constrained environments[J]. IEEE Access, 2018, 6: 32800-32819. doi: 10.1109/ACCESS.2018.2845448
[46]	ZHANG Y, BAI Y, HU J, et al. Control design, stability analysis, and traffic flow implications for cooperative adaptive cruise control systems with compensation of communication delay[J]. Transportation Research Record, 2020, 2674(8): 638-652. doi: 10.1177/0361198120918873
[47]	杨炜, 孙雪, 司宇, 等. 前车切入与切出工况下匀质队列速度规划与跟随控制[J]. 交通运输工程学报, 2024, 24(6): 243-258. doi: 10.19818/j.cnki.1671-1637.2024.06.017 YANG Wei, SUN Xue, SI Yu, et al. Homogeneous platoon speed planning and following control in front vehicle cut-in and cut-out conditions[J]. Journal of Traffic and Transportation Engineering, 2024, 24(6): 243-258. doi: 10.19818/j.cnki.1671-1637.2024.06.017
[48]	张振, 赖金涛, 杨晓光. 基于异质车辆群体协同的特殊车辆优先控制方法[J]. 交通运输工程学报, 2025, 25(2): 156-169. doi: 10.19818/j.cnki.1671-1637.2025.02.010 ZHANG Zhen, LAI Jin-tao, YANG Xiao-guang. A priority control method for special vehicles based on heterogeneous vehicle group cooperation[J]. Journal of Traffic and Transportation Engineering, 2025, 25(2): 156-169. doi: 10.19818/j.cnki.1671-1637.2025.02.010