The path control gives the target path through planning, and uses the tracking strategy to make the vehicle converge to the target path. How to balance the tracking performance and the vehicle stability is a crucial and worthy research for the autonomous vehicle safety. In this paper, a hierarchical path control strategy consists of path planning and tracking with the consideration of vehicle lateral stability is proposed. In the adaptive preview distance block, the preview distance is adaptively regulated according to the vehicle speed, sideslip angle, and the preview trajectory curvature to balance the tracking error and stability. In the path planning block, the three-order polynomial fitting method is adopted to give the desired path according to the preview distance and the relative position relationship between vehicle and road or obstacles. The linear quadratic regulator (LQR) controller is designed to tracking the desired path fully using the previewed curvatures and the vehicle motion error. The hardware in the loop (HIL) simulation and vehicle test results illustrate that the proposed strategy can deal with path tracking, avoidance and lance change scenes in low computation burden, and maintain the stability of vehicle simultaneously.
LiuZPeiXChenZ, et al. An optimal path tracking architecture for automated vehicle. Proc IMechE, Part D: J Automobile Engineering2019; 233(9): 2352–2361.
2.
HuCWangRYanF, et al. Output constraint control on path following of four-wheel independently actuated autonomous ground vehicles. IEEE Trans Vehicular Technol2016; 65(6): 4033–4043.
3.
NaranjoJEGonzalezCGarciaR, et al. Lane-change fuzzy control in autonomous vehicles for the overtaking maneuver. IEEE Trans Intell Transp Syst2008; 9(3): 438–450.
4.
HimaSGlaserSChaibetA, et al. Controller design for trajectory tracking of autonomous passenger vehicles. In: 2011 14th international IEEE conference on intelligent transportation systems (ITSC), pp.1459–1464. IEEE.
5.
PatleBPandeyAParhiD, et al. A review: on path planning strategies for navigation of mobile robot. Def Technol2019; 15(4): 582–606.
6.
JiJKhajepourAMelekWW, et al. Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints. IEEE Trans Vehicular Technol2017; 66(2): 952–964.
7.
KunchevVJainLIvancevicV, et al. Path planning and obstacle avoidance for autonomous mobile robots: a review. In: Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science. 2006. Berlin, Heidelberg: Springer.
8.
KanayamaYJHartmanBI.Smooth local-path planning for autonomous vehicles. Int J Rob Res1997; 16(3): 263–284.
9.
ZhuQYanYXingZ. Robot path planning based on artificial potential field approach with simulated annealing. In: Sixth international conference on intelligent systems design and applications, Jian, China, vol. 2, pp.622–627. IEEE.
10.
Abdul HamidUZZamzuriHYamadaT, et al. Modular design of artificial potential field and nonlinear model predictive control for a vehicle collision avoidance system with move blocking strategy. Proc IMechE, Part D: J Automobile Engineering2018; 232(10): 1353–1373.
11.
CarrollKPMcClaranSRNelsonEL, et al. AUV path planning: an a* approach to path planning with consideration of variable vehicle speeds and multiple, overlapping, time-dependent exclusion zones. In: Proceedings of the 1992 symposium on autonomous underwater vehicle technology, Washington, DC, pp.79–84. IEEE.
12.
LinFWangSZhaoY, et al. Research on autonomous vehicle path tracking control considering roll stability. Proc IMechE, Part D: J Automobile Engineering2021; 235(1): 199–210.
13.
XiangXYuCZhangQ.Robust fuzzy 3d path following for autonomous underwater vehicle subject to uncertainties. Comput Oper Res2017; 84:165–177.
14.
YuCXiangXLapierreL, et al. Nonlinear guidance and fuzzy control for three-dimensional path following of an underactuated autonomous underwater vehicle. Ocean Eng2017; 146:457–467.
15.
AntonelliGChiaveriniSFuscoG.A fuzzy-logic-based approach for mobile robot path tracking. IEEE Trans Fuzzy Syst2007; 15(2): 211–221.
16.
SoleaRNunesU.Trajectory planning and sliding-mode control based trajectory-tracking for cybercars. Integr Comput Aided Eng2007; 14(1): 33–47.
17.
WangJSteiberJSurampudiB.Autonomous ground vehicle control system for high-speed and safe operation. Int J Veh Auton Syst2009; 7(1-2): 18–35.
18.
GuoLGePYangX, et al. Intelligent vehicle trajectory tracking based on neural networks sliding mode control. In: Proceedings 2014 international conference on informative and cybernetics for computational social systems (ICCSS), Qingdao, China, pp.57–62. IEEE.
19.
RaffoGVGomesGKNormey-RicoJE, et al. A predictive controller for autonomous vehicle path tracking. IEEE Trans Intell Transp Syst2009; 10(1): 92–102.
20.
DaiCZongCChenG.Path tracking control based on model predictive control with adaptive preview characteristics and speed-assisted constraint. IEEE Access2020; 8:184697–184709.
21.
CaiJJiangHChenL, et al. Implementation and development of a trajectory tracking control system for intelligent vehicle. J Intell Robot Syst2019; 94(1): 251–264.
22.
SymondsPSharpRSCasanovaD.A mathematical model for driver steering control, with design, tuning and performance results. Veh Syst Dyn2000; 33(5): 289–326.
23.
SharpRS.Driver steering control and a new perspective on car handling qualities. Proc IMechE, Part C: J Mechanical Engineering Science2005; 219(10): 1041–1051.
24.
SharpRS.Rider control of a motorcycle near to its cornering limits. Veh Syst Dyn2012; 50(8): 1193–1208.
25.
XuSPengH.Design, analysis, and experiments of preview path tracking control for autonomous vehicles. IEEE Trans Intell Transp Syst2020; 21(1): 48–58.
26.
XiangCPengHWangW, et al. Path tracking coordinated control strategy for autonomous four in-wheel-motor independent-drive vehicles with consideration of lateral stability. Proc IMechE, Part D: J Automobile Engineering2021; 235(4): 1023–1036.
27.
ChenCJiaYShuM, et al. Hierarchical adaptive path-tracking control for autonomous vehicles. IEEE Trans Intell Transp Syst2015; 16(5): 2900–2912.
28.
LiSEChenHLiR, et al. Predictive lateral control to stabilise highly automated vehicles at tire-road friction limits. Veh Syst Dyn2020; 58(5): 768–786.
29.
HeHShiMLiJ, et al. Design and experiential test of a model predictive path following control with adaptive preview for autonomous buses. Mech Syst Signal Process2021; 157:107701.
