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Abstract

Autonomous driving is a complex and large-scale system, where trajectory planning and trajectory tracking of vehicles are key technologies. This paper primarily focuses on structured road scenarios and the real-time handling of various dynamic obstacles. Based on the idea of decoupling speed and path, the 3D optimization problem, which includes time-speed and time-position information, is decomposed into two 2D problems. Firstly, vehicle behavior decisions are made using dynamic programing, followed by optimization to determine the path and speed. Secondly, we design SL and ST iterative frameworks to handle obstacles. Then we compare DP-QP with NMPC to validate the efficiency of the algorithms. Thirdly, a two dof error dynamics model predictive tracking controller is designed for path tracking. A nonlinear longitudinal dynamics controller is developed using the Gauss pseudospectral method to achieve velocity tracking. Compared to the nonlinear model predictive controller, improvements are achieved in both speed calculation and control accuracy. Finally, the proposed methods are validated using a joint simulation platform consisting of PreScan, MATLAB/Simulink, and CarSim. The results demonstrate that the proposed approach can generate reasonable and smooth trajectories while successfully avoiding obstacles. The execution frequencies of the planner and controller meet the requirements for autonomous driving.

Keywords

optimal control trajectory planning trajectory tracking dynamic obstacle avoidance simulation validation

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References

Dong

Gao

Chen

, et al. Real-time velocity optimization for energy-efficient control of connected and automated vehicles. J Dyn Syst Meas Control 2022; 144(1): 210–220.

Olayode

Severino

, et al. Systematic literature review on the applications, impacts, and public perceptions of autonomous vehicles in road transportation system. J Traffic Transp Eng 2023; 10: 1037–1060.

Rong

Meng

Guo

, et al. Multi-vehicle collaborative planning technology under automatic driving. Sustainability 2024; 16(11): 4578–4596.

Hang

Huang

Chen

, et al. Path planning of collision avoidance for unmanned ground vehicles: a nonlinear model predictive control approach. Proc Inst Mech Eng Part I: J Syst Control Eng 2021; 235(2): 222–236.

Katona

Neamah

Korondi

. Obstacle avoidance and path planning methods for autonomous navigation of mobile robot. Sensors 2024; 24(11): 3573–3620.

Liu

Zhao

Tan

, et al. Global path planning for autonomous vehicles in off-road environment via an astar algorithm. Int J Veh Auton Syst 2017; 13(4): 330–339.

Huang

Research on path planning algorithm of autonomous vehicles based on improved RRT algorithm. Int J Intell Transp Syst Res 2022; 20(1): 170–180.

Yang

, et al. Collision avoidance for autonomous vehicles based on MPC with adaptive apf. IEEE Trans Intell Vehicles 2024; 9(1): 1559–1570.

Missura

Bennewitz

Predictive collision avoidance for the dynamic window approach. In: International Conference on Robotics and Automation (ICRA), 2019, pp. 8620–8626.

10.

Flores-Aquino

Vasquez-Gomez

Gutierrez-Frias

. Custom distribution for sampling-based motion planning. J Braz Soc Mech Sci Eng 2022; 44(3): 73–88.

11.

Ammour

Orjuela

Basset

A MPC combined decision making and trajectory planning for autonomous vehicle collision avoidance. IEEE Trans Intell Transp Syst 2022; 23(12): 24805–24817.

12.

Liu

Yang

, et al. An optimization-based path planning approach for autonomous vehicles using the DynEFWA-artificial potential field. IEEE Trans Intell Vehicles 2022; 7(2): 263–272.

13.

Yang

, et al. Construction of novel self-adaptive dynamic window approach combined with fuzzy neural network in complex dynamic environments. IEEE Access 2022; 10: 104375–104383.

14.

Zhang

Chu

Liu

, et al. Trajectory planning and tracking for autonomous vehicle based on state lattice and model predictive control. IEEE Intell Transp Syst Mag 2019; 11(2): 29–40.

15.

Werling

Ziegler

Kammel

, et al. Optimal trajectory generation for dynamic street scenarios in a frenet frame. In: IEEE international conference on robotics and automation, 2010, pp. 987–993.

16.

McNaughton

Urmson

Dolan

, et al. Motion planning for autonomous driving with a conformal spatiotemporal lattice. In: IEEE international conference on robotics and automation, 2011, pp. 4889–4895.

17.

, et al. Dynamic trajectory planning and tracking for autonomous vehicle with obstacle avoidance based on model predictive control. IEEE Access 2019; 7: 132074–132086.

18.

Acarman

Peng

, et al. Maneuver planning for automatic parking with safe travel corridors: a numerical optimal control approach. In: 2020 European control conference, 2020, pp. 1993–1998. IEEE.

19.

Viana

Kanchwala

Ahiska

, et al. A comparison of trajectory planning and control frameworks for cooperative autonomous driving. J Dyn Syst Meas Control 2021; 143(7): 1–9.

20.

Liu

Chen

, et al. Vehicle path replanning for obstacle avoidance considering driving styles. In: 2023 7th CAA international conference on vehicular control and intelligence (CVCI), 2023, pp. 1–6. IEEE.

21.

Chu

Zhao

, et al. Trajectory tracking of autonomous vehicle based on model predictive control with pid feedback. IEEE Trans Intell Transp Syst 2022; 24(2): 1–12.

22.

Sun

Dai

Liu

, et al. Intelligent vehicle path tracking based on feedback linearization and LOR under extreme conditions. In: 2022 41st Chinese control conference (CCC), 2022, pp. 5383–5389. IEEE.

23.

Jond

Platos

A fast trajectory tracking control design for autonomous driving. In: 7th international conference on robotics and mechatronics, 2019, pp. 1–6. IEEE.

24.

Yue

Hou

Fan

, et al. Coordinated trajectory tracking control for an underactuated tractor-trailer vehicle via MPC and SMC approaches. In: 2017 2nd international conference on advanced robotics and mechatronics (ICARM), 2017, pp. 82–87. IEEE.

25.

Hua

Peng

Mou

, et al. Model prediction control path tracking algorithm based on adaptive stanley. In: 2022 IEEE 96th vehicular technology conference, 2022, pp. 1–5.

26.

Elbanhawi

Simic

Jazar

Receding horizon lateral vehicle control for pure pursuit path tracking. J Vib Control 2018; 24(3): 619–642.

27.

Kanchwala

Path planning and tracking of an autonomous car with high fidelity vehicle dynamics model and human driver trajectories. In: 2019 IEEE 10th international conference on mechanical and aerospace engineering (ICMAE), 2019, pp. 306–313. IEEE.

28.

Kanchwala

Bezerra Viana

Aouf

. Cooperative path-planning and tracking controller evaluation using vehicle models of varying complexities. Proc Inst Mech Eng Part C: J Mech Eng Sci 2021; 235(16): 2877–2896.

29.

Song

Zhang

Data-driven trajectory-tracking in automated parking system via iterative learning compensation and model predictive control. Proc Inst Mech Eng Part D: J Automobile Eng 2023; 237(5): 1131–1145.

Real-time planning and control methods for autonomous vehicles

Abstract

Keywords

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