This study addresses the challenges of insufficient real-time path planning and limited tracking accuracy for intelligent vehicles in complex dynamic environments by proposing a novel path planning and tracking control framework. The framework integrates an improved A* algorithm for path planning and a model predictive control (MPC) algorithm for tracking control. First, to overcome the limitations of the traditional Hybrid A* algorithm, we introduce the Direction-Hybrid A* algorithm, which incorporates directionality, optimizes the cost function, and refines the heuristic map function. These enhancements significantly improve the efficiency and quality of path planning. Second, for path tracking control, we design a controller based on the MPC algorithm. We develop both a kinematic coupled model predictive controller and a linear dynamics model predictive controller, each tailored to different driving conditions: the former for low-speed scenarios and the latter for medium- and high-speed scenarios. Finally, simulation and real-vehicle experiments confirm that the proposed algorithms achieve shorter planning times and more efficient path planning in low-speed scenarios. They also demonstrate improved stability in path tracking and reduced lateral deviation in medium- and high-speed scenarios. Overall, the improved algorithms significantly enhance the path planning and tracking control performance of intelligent vehicles.
GonzálezDPérezJMilanésV, et al. A review of motion planning techniques for automated vehicles. IEEE Trans Intell Transp Syst2016; 17: 1135–1145.
2.
YouCLuJFilevD, et al. Autonomous planning and control for intelligent vehicles in traffic. IEEE Trans Intell Transp Syst2020; 21: 2339–2349.
3.
LuuDLLupuCBuiVG, et al. RST longitudinal control design for smart car platform in autonomous driving. Stud Inform Control2025; 34: 97–107.
4.
HartPNilssonNRaphaelB.A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern1968; 4: 100–107.
5.
ZhangHWangYZhengJ, et al. Path planning of industrial robot based on improved RRT algorithm in complex environments. IEEE Access2018; 6: 53296–53306.
6.
KhanATLiSKadryS, et al. Control framework for trajectory planning of soft manipulator using optimized RRT algorithm. IEEE Access2020; 8: 171730–171743.
7.
CuiRLiYYanW.Mutual information-based multi-AUV path planning for scalar field sampling using multidimensional RRT*. IEEE Trans Syst Man Cybern Syst2016; 46: 993–1004.
8.
YaoWQiNYueC, et al. Curvature-bounded lengthening and shortening for restricted vehicle path planning. IEEE Trans Autom Sci Eng2020; 17: 15–28.
9.
EirasFHawaslyMAlbrechtS, et al. A two-stage optimization-based motion planner for safe urban driving. IEEE Trans Robot2022; 38: 822–834.
10.
MicheliFBersaniMArrigoniS, et al. NMPC trajectory planner for urban autonomous driving. Veh Syst Dyn2023; 61: 1387–1409.
11.
WangMDongXPanW, et al. Robust path planning of obstacle avoidance for unmanned delivery robots. J Control Eng Appl Inform2023; 25: 60–71.
12.
HarisMNamH.Path planning optimization of smart vehicle with fast converging distance-dependent PSO algorithm. IEEE Open J Intell Transp Syst2024; 5: 726–739.
13.
LiXLiGBianZ.Research on autonomous vehicle path planning algorithm based on improved RRT* algorithm and artificial potential field method. Sensors2024; 24: 3899.
14.
ShanSShaoJZhangH, et al. Research and validation of self-driving path planning algorithm based on optimized A*-artificial potential field method. IEEE Sens J2024; 24: 24708–24722.
15.
LiXLuYZhaoX, et al. Path planning for intelligent vehicles based on improved D* lite. J Supercomput2024; 80: 1294–1330.
16.
ChuLWangYLiS, et al. Intelligent vehicle path planning based on optimized A* algorithm. Sensors2024; 24: 3149.
17.
LongXSunMChenZ.Efficient trajectory planning and tracking control for underactuated crane. J Control Eng Appl Inform2023; 25: 35–43.
18.
CollottaMFerreroRGiustoE, et al. A fuzzy control system for energy-efficient wireless devices in the internet of vehicles. Int J Intell Syst2021; 36: 1595–1618.
19.
AkermiKBoudaaBChouraquiS.A nonlinear robust controller design for path following of intelligent electrical vehicle. J Control Eng Appl Inform2025; 27: 15–25.
20.
DoğmuşOGüneşM.Trajectory and speed control of tracked mobile robot with interval type-2 TSK fuzzy logic controller. Stud Inform Control2024; 33: 39–50.
21.
Al-GabalawyMHosnyNAborishaA.Model predictive control for a basic adaptive cruise control. Int J Dyn Control2021; 9: 1132–1143.
22.
ShamseldinMA.An efficient single neuron PID-sliding mode tracking control for simple electric vehicle model. J Appl Nonlinear Dyn2022; 11: 1–15.
23.
WangGLiuLMengY, et al. Integrated path tracking control of steering and braking based on holistic MPC. IFAC-Pap OnLine2021; 54: 45–50.
24.
YuZTangXLvX, et al. Predictive control via augmented Lagrange function for autonomous vehicles trajectory tracking with dynamic quantization. J Control Eng Appl Inform2024; 26: 14–24.
25.
XuYZhangFSunY.Intelligent assisted driving method based on mathematical modeling and MPC algorithm. Stud Inform Control2025; 34: 15–25.
26.
ShiPLiLNiX, et al. Intelligent vehicle path tracking control based on improved MPC and hybrid PID. IEEE Access2022; 10: 94133–94144.
27.
WuYWangLLiF, et al. Robust sliding mode prediction path tracking control for intelligent vehicle. Proc Inst Mech Eng Part I: J Syst Control Eng2022; 236: 1607–1617.
28.
ChuDLiHZhaoC, et al. Trajectory tracking of autonomous vehicle based on model predictive control with PID feedback. IEEE Trans Intell Transp Syst2023; 24: 2239–2250.
29.
TangLYanFZouB, et al. An improved kinematic model predictive control for high-speed path tracking of autonomous vehicles. IEEE Access2020; 8: 51400–51413.
30.
ChenQYuBMinS, et al. Study on intelligent vehicle trajectory planning and tracking control based on improved APF and MPC. Int J Automot Technol2025; 26: 715–728.
31.
LiJLouJLiY, et al. Trajectory tracking of autonomous vehicle using clothoid curve. Appl Sci2023; 13: 2733.
32.
ZhangPXiongLYuZ, et al. Reinforcement learning-based end-to-end parking for automatic parking system. Sensors2019; 19: 3996.
33.
LiCJiangHMaS, et al. Automatic parking path planning and tracking control research for intelligent vehicles. Appl Sci2020; 10: 9100.
34.
AghabaykKForouzidehNYoungW.Exploring a local linear model tree approach to car-following. Comput Aided Civil Infrastruct Eng2013; 28: 581–593.
35.
HePFanPWuS, et al. Research on path planning based on bidirectional A* algorithm. IEEE Access2024; 12: 109625–109633.
36.
GaoJXuXPuQ, et al. A hybrid path planning method based on improved A* And CSA-APF algorithms. IEEE Access2024; 12: 39139–39151.
37.
LiDYinWWongW, et al. Quality-oriented hybrid path planning based on A* and Q-learning for unmanned aerial vehicle. IEEE Access2022; 10: 7664–7674.
38.
HignasariLMahiraED. Optimization of goods distribution route assisted by Google map with cheapest insertion heuristic algorithm (CIH). Sinergi2018; 22: 132–138.
39.
LiYJinRXuX, et al. A mobile robot path planning algorithm based on improved A* algorithm and dynamic window approach. IEEE Access2022; 10: 57736–57747.
40.
WangHQiXLouS, et al. An efficient and robust improved A* algorithm for path planning. Symmetry2021; 13: 2213.
41.
DuYLiuCLiY.Velocity control strategies to improve automated vehicle driving comfort. IEEE Intell Transp Syst Mag2018; 10: 8–18.
42.
SubositsJGerdesJ.Impacts of model fidelity on trajectory optimization for autonomous vehicles in extreme maneuvers. IEEE Trans Intell Veh2021; 6: 546–558.
43.
RokonuzzamanMMohajerNNahavandiS, et al. Model predictive control with learned vehicle dynamics for autonomous vehicle path tracking. IEEE Access2021; 9: 128233–128249.
44.
SunZWangRMengX, et al. A novel path tracking system for autonomous vehicle based on model predictive control. J Mech Sci Technol2024; 38: 365–378.
45.
LiSLiZYuZ, et al. Dynamic trajectory planning and tracking for autonomous vehicle with obstacle avoidance based on model predictive control. IEEE Access2019; 7: 132074–132086.
46.
SunCZhangXZhouQ, et al. A model predictive controller with switched tracking error for autonomous vehicle path tracking. IEEE Access2019; 7: 53103–53114.
47.
ZhangWCuiZWangN, et al. Research on path planning algorithm based on fast target detection. J Artif Intell Pract2024; 7: 173–181.
