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A Hierarchical Framework of Decision Making and Trajectory Tracking Control for Autonomous Vehicles

Author

Listed:
  • Tao Wang

    (School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Dayi Qu

    (School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Hui Song

    (School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Shouchen Dai

    (School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China)

Abstract

Most of the existing research in the field of autonomous vehicles (AVs) addresses decision making, planning and control as separate factors which may affect AV performance in complex driving environments. A hierarchical framework is proposed in this paper to address the problem mentioned above in environments with multiple lanes and surrounding vehicles. Firstly, high-level decision making is implemented by a finite-state machine (FSM), according to the relative relationship between the ego vehicle (EV) and the surrounding vehicles. After the decision is made, a cluster of quintic polynomial equations is established to generate the path connecting the initial position to the candidate target positions, according to the traffic states of the EV and the target vehicle. The optimal path is selected from the cluster, based on the quadratic programming (QP) framework. Then, the speed profile is generated, based on the longitudinal displacement–time graph (S–T graph), considering the vehicle motion state constraints and collision avoidance. The smoothed speed profile is created through another QP formulation, in the space created by the dynamic-programming (DP) method. Finally, the planned path and speed profile are combined and sent to the lower level control module, which consists of the linear quadratic regulator (LQR) for lateral trajectory tracking control, and a double PID controller for longitudinal control. The performance of the proposed framework was validated in a co-simulation environment using PreScan, MATLAB/Simulink and CarSim, and the results demonstrate that the proposed framework is capable of addressing most ordinary scenarios on a structured road, with reasonable decisions and controlling abilities.

Suggested Citation

  • Tao Wang & Dayi Qu & Hui Song & Shouchen Dai, 2023. "A Hierarchical Framework of Decision Making and Trajectory Tracking Control for Autonomous Vehicles," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6375-:d:1118436
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    References listed on IDEAS

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    1. Junyan Han & Xiaoyuan Wang & Gang Wang, 2022. "Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review," Sustainability, MDPI, vol. 14(13), pages 1-27, July.
    2. Pengwei Wang & Song Gao & Liang Li & Binbin Sun & Shuo Cheng, 2019. "Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm," Energies, MDPI, vol. 12(12), pages 1-14, June.
    3. Ying Wang & Chong Wei, 2020. "A Universal Trajectory Planning Method for Automated Lane-Changing and Overtaking Maneuvers," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, April.
    4. Liu, Yang & Wu, Fanyou & Lyu, Cheng & Li, Shen & Ye, Jieping & Qu, Xiaobo, 2022. "Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
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    1. Yuansheng Cao & Yonggang Liao & Jiancong Lai & Tianjie Shen & Xiaofei Wang, 2024. "Study on the Deviation Characteristics of Driving Trajectories for Autonomous Vehicles and the Design of Dedicated Lane Widths," Sustainability, MDPI, vol. 16(21), pages 1-18, October.

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