Volume 26 Issue 6
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LEI Ming-yue, LAI Jin-tao, HU Jia. Infrastructure enhanced motion planning for automatic driving on unstructured roads: A case study of ETC stations[J]. Journal of Traffic and Transportation Engineering, 2026, 26(6): 123-136. doi: 10.19818/j.cnki.1671-1637.2026.078
Citation: LEI Ming-yue, LAI Jin-tao, HU Jia. Infrastructure enhanced motion planning for automatic driving on unstructured roads: A case study of ETC stations[J]. Journal of Traffic and Transportation Engineering, 2026, 26(6): 123-136. doi: 10.19818/j.cnki.1671-1637.2026.078
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Infrastructure enhanced motion planning for automatic driving on unstructured roads: A case study of ETC stations

doi: 10.19818/j.cnki.1671-1637.2026.078
  • 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
  • Received Date: 2025-03-20
  • Accepted Date: 2025-09-26
  • Rev Recd Date: 2025-08-27
  • Publish Date: 2026-06-28
    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.

     

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    • References(48)
  • [1]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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
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