Volume 26 Issue 4
Apr.  2026
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CAI Ying-feng, ZHANG Yu-hang, SUN Xiao-qiang, ZHANG Xiao-dong, WANG Hai, CHEN Long. Trajectory tracking control technology for autonomous vehicles based on dynamic Kalman polynomial networks[J]. Journal of Traffic and Transportation Engineering, 2026, 26(4): 303-318. doi: 10.19818/j.cnki.1671-1637.2026.021
Citation: CAI Ying-feng, ZHANG Yu-hang, SUN Xiao-qiang, ZHANG Xiao-dong, WANG Hai, CHEN Long. Trajectory tracking control technology for autonomous vehicles based on dynamic Kalman polynomial networks[J]. Journal of Traffic and Transportation Engineering, 2026, 26(4): 303-318. doi: 10.19818/j.cnki.1671-1637.2026.021
shu

Trajectory tracking control technology for autonomous vehicles based on dynamic Kalman polynomial networks

doi: 10.19818/j.cnki.1671-1637.2026.021
  • 1.

    Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, Jiangsu, China

  • 2.

    Geely Automobile Research Institute (Ningbo) Co., Ltd., Ningbo 315336, Zhejiang, China

  • 3.

    School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China

Funds:

National Natural Science Foundation of China 52225212

National Natural Science Foundation of China 52272418

National Natural Science Foundation of China U22A20100

National Key R&D Program of China 2022YFB2503302

More Information
  • Corresponding author: CAI Ying-feng, professor, PhD, E-mail: caicaixiao0304@126.com
  • Received Date: 2025-02-14
  • Accepted Date: 2025-08-25
  • Rev Recd Date: 2025-07-13
  • Publish Date: 2026-04-28
    Abstract
  • To achieve high-reliability and high-precision trajectory tracking control for autonomous vehicles in complex dynamic scenarios, a predictive control framework based on a dynamic Kalman polynomial network was proposed. The dynamic characteristics of vehicles were captured by utilizing time-series memory capabilities, and the adaptive adjustment of Kalman filter was integrated to achieve the optimal correction of real-time control errors. By utilizing the nonlinear feature extension of polynomial networks, the modeling capability of the system was further enhanced. Dynamic perception, error correction, and nonlinear modeling were integrated into the method, significantly enhancing the adaptability and precision of the system to vehicle dynamics variations. Furthermore, a feedback controller based on lateral and heading deviations was incorporated to work synergistically with the predictive module, thereby achieving more accurate path tracking error correction. The co-simulation verification of CarSim and Simulink shows that the predictive-feedback controller exhibits obvious advantages under extreme conditions of low-adhesion surfaces and high-speed emergency lane changes. Specifically, under the condition of high-speed emergency lane changes, compared with ILQR and NMPC controllers, the root mean square value of the lateral deviation of path tracking is reduced by 32.5% and 38.4%, respectively; the root mean square value of heading deviation is reduced by 37.8% and 40.0%, respectively. The proposed method can provide an innovative and effective solution for the high-precision and high-reliability trajectory tracking control in autonomous driving systems.

     

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    • References(33)
  • [1]
    JU Z Y, ZHANG H, LI X, et al. A survey on attack detection and resilience for connected and automated vehicles: From vehicle dynamics and control perspective[J]. IEEE Transactions on Intelligent Vehicles, 2022, 7(4): 815-837. doi: 10.1109/TIV.2022.3186897
    [2]
    CHENG S, WANG Z, YANG B, et al. Convolutional neural network-based lane-change strategy via motion image representation for automated and connected vehicles[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(9): 12953-12964. doi: 10.1109/TNNLS.2023.3265662
    [3]
    ZHAO Xiang-mo's team supported by the National Key Research and Development Program of China(2021YFB2501200). Research progress in testing and evaluation technologies for autonomous driving[J]. Journal of Traffic and Transportation Engineering, 2023, 23(6): 10-77. doi: 10.19818/j.cnki.1671-1637.2023.06.002
    [4]
    LI Jie, ZHANG Luo-wei, WANG Xiao-yan, et al. A multi-sensor fusion algorithm based on view-cone distance and adaptive weighted Kalman filter[J]. China Journal of Highway and Transport, 2024, 37(3): 194-203.
    [5]
    ZHAO Y Y, WANG L, YUN X Y, et al. Enhanced scene understanding and situation awareness for autonomous vehicles based on semantic segmentation[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024, 54(11): 6537-6549. doi: 10.1109/TSMC.2024.3403859
    [6]
    HE X K, YANG H L, HU Z X, et al. Robust lane change decision making for autonomous vehicles: An observation adversarial reinforcement learning approach[J]. IEEE Transactions on Intelligent Vehicles, 2023, 8(1): 184-193. doi: 10.1109/TIV.2022.3165178
    [7]
    LI Sheng-qin, DING Xue-mei. Trajectory planning of intelligent vehicle based on quintic polynomial[J]. Journal of Jiangsu University (Natural Science Edition), 2023, 44(4): 392-398.
    [8]
    SHI X Y, WANG H, CHEN L, et al. A distributed driving six-wheel steering commercial vehicle chassis stability domain criterion for coordination of multiple subsystems[J]. IEEE Transactions on Vehicular Technology, 2024, 73(12): 18512-18526. doi: 10.1109/TVT.2024.3445597
    [9]
    ZHAO Xuan, WANG Shu, MA Jian, et al. Review of chassis integrated control technology for distributed drive electric vehicles[J]. China Journal of Highway and Transport, 2023, 36(4): 221-248.
    [10]
    HUANG Yi-shao, ZHUANG Di. Vehicle longitudinal control based on disturbance observer and terminal sliding mode[J]. Journal of Jiangsu University (Natural Science Edition), 2024, 45(5): 513-520.
    [11]
    PAN Shi-ju, LI Yong-le, LI Zi-xian, et al. Path following method of intelligent vehicles based on improved pure tracking[J]. Automotive Engineering, 2023, 45(1): 1-8, 19.
    [12]
    BAI Guo-xing, MENG Yu, LIU Li, et al. Current status of path tracking control of unmanned driving vehicles[J]. Chinese Journal of Engineering, 2021, 43(4): 475-485.
    [13]
    MARINO R, SCALZI S, NETTO M. Nested PID steering control for lane keeping in autonomous vehicles[J]. Control Engineering Practice, 2011, 19(12): 1459-1467. doi: 10.1016/j.conengprac.2011.08.005
    [14]
    CHU D F, LI H R, ZHAO C Y, et al. Trajectory tracking of autonomous vehicle based on model predictive control with PID feedback[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(2): 2239-2250.
    [15]
    HU C, WANG R R, YAN F J, et al. Output constraint control on path following of four-wheel independently actuated autonomous ground vehicles[J]. IEEE Transactions on Vehicular Technology, 2016, 65(6): 4033-4043. doi: 10.1109/TVT.2015.2472975
    [16]
    JUNG H, JUNG D, CHOI S B. LQR control of an all-wheel drive vehicle considering variable input constraint[J]. IEEE Transactions on Control Systems Technology, 2022, 30(1): 85-96. doi: 10.1109/TCST.2021.3051233
    [17]
    NAM H, CHOI W, AHN C. Model predictive control for evasive steering of an autonomous vehicle[J]. International Journal of Automotive Technology, 2019, 20(5): 1033-1042. doi: 10.1007/s12239-019-0097-5
    [18]
    ZHAI L, WANG C P, HOU Y H, et al. MPC-based integrated control of trajectory tracking and handling stability for intelligent driving vehicle driven by four hub motor[J]. IEEE Transactions on Vehicular Technology, 2022, 71(3): 2668-2680. doi: 10.1109/TVT.2022.3140240
    [19]
    SHI X Y, WANG H, CHEN L, et al. Robust path tracking control of distributed driving six-wheel steering commercial vehicle based on coupled active disturbance rejection[J]. IEEE Transactions on Vehicular Technology, 2023, 72(11): 13940-13952.
    [20]
    HANG P, CHEN X B. Integrated chassis control algorithm design for path tracking based on four-wheel steering and direct yaw-moment control[J]. Proceedings of the Institution of Mechanical Engineers, Part Ⅰ: Journal of Systems and Control Engineering, 2019, 233(6): 625-641.
    [21]
    SPIELBERG N A, BROWN M, KAPANIA N R, et al. Neural network vehicle models for high-performance automated driving[J]. Science Robotics, 2019, 4(28): eaaw1975. doi: 10.1126/scirobotics.aaw1975
    [22]
    DA LIO M, BORTOLUZZI D, PAPINI G P R. Modelling longitudinal vehicle dynamics with neural networks[J]. Vehicle System Dynamics, 2020, 58(11): 1675-1693. doi: 10.1080/00423114.2019.1638947
    [23]
    BECKERS T, COLOMBO L J, HIRCHE S, et al. Online learning-based trajectory tracking for underactuated vehicles with uncertain dynamics[J]. IEEE Control Systems Letters, 2021, 6: 2090-2095.
    [24]
    YU H X, DUAN J M, TAHERI S, et al. A model predictive control approach combined unscented Kalman filter vehicle state estimation in intelligent vehicle trajectory tracking[J]. Advances in Mechanical Engineering, 2015, 7(5): 1-14.
    [25]
    LI L, WANG T Q, XIA Y Q, et al. Trajectory tracking control for wheeled mobile robots based on nonlinear disturbance observer with extended Kalman filter[J]. Journal of the Franklin Institute, 2020, 357(13): 8491-8507. doi: 10.1016/j.jfranklin.2020.04.043
    [26]
    ZHANG Q, BHATTARAIN, CHEN H, et al. Vehicle trajectory tracking using adaptive Kalman filter from roadside lidar[J]. Journal of Transportation Engineering, Part A: Systems, 2023, 149(6): 04023043. doi: 10.1061/JTEPBS.TEENG-7535
    [27]
    CHEN Te, CHEN Long, XU Xing, et al. Integrated control of unmanneddistributed driven vehicles path tracking and stability[J]. Automotive Engineering, 2019, 41(10): 1109-1116.
    [28]
    SHI X Y, WANG H, CHEN L, et al. Hybrid trigger cooperative control of six-wheeled commercial vehicles with multiple sub-systems based on sub-regional linearization model[J]. Simulation Modelling Practice and Theory, 2024, 135: 102973. doi: 10.1016/j.simpat.2024.102973
    [29]
    HOU R F, ZHAI L, SUN T M, et al. Steering stability control of a four in-wheel motor drive electric vehicle on a road with varying adhesion coefficient[J]. IEEE Access, 2019, 7: 32617-32627. doi: 10.1109/ACCESS.2019.2901058
    [30]
    MA Yong-jie, CHENG Shi-sheng, MA Yun-ting, et al. Review of convolutional neural network and its application in intelligent transportation system[J]. Journal of Traffic and Transportation Engineering, 2021, 21(4): 48-71. doi: 10.19818/j.cnki.1671-1637.2021.04.003
    [31]
    WU Xiao, SHI Wen-ku, CHEN Zhi-yong. Active suspension control based on interacting multiple model Kalman filter[J]. Automotive Engineering, 2023, 45(7): 1200-1211, 1253.
    [32]
    HU Jing-yu, WANG Yan, YAN Yong-jun, et al. Vehicle state estimation based on limited memory random weighted extended Kalman filter[J]. Journal of Southeast University (Natural Science Edition), 2022, 52(2): 387-393.
    [33]
    KAPANIA N R, GERDES J C. Design of a feedback-feedforward steering controller for accurate path tracking and stability at the limits of handling[J]. Vehicle System Dynamics, 2015, 53(12): 1687-1704. doi: 10.1080/00423114.2015.1055279
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