This article addresses the coordinated longitudinal and lateral motion control for an intelligent vehicle highway system. The strategy of this work consists of defining the edges of the traveled lane using a vision sensor. According to the detected boundaries, a constrained path-following method is proposed to drive the longitudinal and the lateral vehicle’s motion. Error constraints of the intelligent vehicle highway system position are manipulated by including the function of barrier Lyapunov in designing the guidance algorithm for the intelligent vehicle highway system. To calculate the necessary forces that would steer the vehicle to the desired path, a control design is proposed that integrates the sign of the error for the compensation of the uncertain vehicle’s parameters. The Lyapunov function is later used to minimize the path-following errors and to guarantee a stable system. The efficiency of the developed approach is proved by numerical simulations.
HingwePMasayoshiT. A variable look-ahead controller for lateral guidance of four wheeled vehicles. In: Proceedings of the American control conference, Philadelphia, PA, 26 June 1998, Vol. 1. New York: IEEE.
2.
AttiaRRodolfoOMichelB. Combined longitudinal and lateral control for automated vehicle guidance. Vehicle Syst Dyn2014; 52(2): 261–279.
3.
GuoJLiLLiKet al. An adaptive fuzzy-sliding lateral control strategy of automated vehicles based on vision navigation. Vehicle Syst Dyn2013; 51(10): 1502–1517.
4.
SoteloMA. Nonlinear lateral control of vision driven autonomous vehicles. Int J Mach Intell Robot Control2003; 5(3): 87–93.
5.
HuCJingHWangRet al. Robust H output-feedback control for path following of autonomous ground vehicles. Mech Syst Signal Pr2016; 70: 414–427.
6.
RajamaniR. Vehicle dynamics and control. Berlin: Springer, 2011.
7.
KehtarnavazNGroswoldNMillerKet al. A transportable neural-network approach to autonomous vehicle following. IEEE T Veh Technol1998; 47(2): 694–702.
8.
LeeHMasayoshiT. Coordinated longitudinal and lateral motion control of vehicles for IVHS. J Dyn Syst Meas Control2001; 123(3): 535–543.
9.
PhamHKarlHMasayoshiT. Combined lateral and longitudinal control of vehicles for IVHS. In: Proceedings of the American control conference, Baltimore, MD, 29 June–1 July 1994, Vol. 2. New York: IEEE.
10.
LiXSunZCaoDet al. Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles. Mech Syst Signal Pr2017; 87: 118–137.
11.
LimEM. Lateral and longitudinal vehicle control coupling in the automated highway system. Master’s Thesis, University of California Berkeley, Berkeley, CA, 1998.
12.
MenhourLD’Ándrea-NovelBBoussardCet al. Algebraic nonlinear estimation and flatness-based lateral/longitudinal control for automotive vehicles. In: 14th IEEE international conference on intelligent transportation systems (ITSC), Washington, DC, 5–7 October 2011. New York: IEEE.
13.
NehaouaLLydieN. Backstepping based approach for the combined longitudinal-lateral vehicle control. In: IEEE intelligent vehicles symposium (IV), Alcala de Henares, 3–7 June 2012. New York: IEEE.
14.
ZhaoFGeSSTuFet al. Adaptive neural network control for active suspension system with actuator saturation. IET Control Theory A2016; 10(14): 1696–1705.
15.
ChenMShuzhiSG. Adaptive neural output feedback control of uncertain nonlinear systems with unknown hysteresis using disturbance observer. IEEE T Ind Electron2015; 62(12): 7706–7716.
16.
KumarawaduSTsuTL. Neuroadaptive combined lateral and longitudinal control of highway vehicles using RBF networks. IEEE T Intell Transp2006; 7(4): 500–512.
17.
KumarawaduSTsuTL. Neuroadaptive output tracking of fully autonomous road vehicles with an observer. IEEE T Intell Transp2009; 10(2): 335–345.
18.
ChiangHHMaLSPerngJWet al. Longitudinal and lateral fuzzy control systems design for intelligent vehicles. In: IEEE international conference on networking, sensing and control (ICNSC’06), Fort Lauderdale, FL, 23–25 April 2006. New York: IEEE.
19.
OsmanKGhommamJSaadM. Combined road following control and automatic lane keeping for automated guided vehicles. In: 14th IEEE international conference on control, automation, robotics and vision (ICARCV), Phuket, Thailand, 13–15 November 2016. New York: IEEE.
20.
MoghadamPJunFD. Road direction detection based on vanishing-point tracking. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), Vilamoura, 7–12 October 2012. New York: IEEE.
21.
LiCDaiBWangRet al. Multi-lane detection based on omnidirectional camera using anisotropic steerable filters. IET Intell Transp Sy2016; 10(5): 298–307.
22.
CualainDOGlavinMJonesE. Multiple-camera lane departure warning system for the automotive environment. IET Intell Transp Sy2012; 6(3): 223–234.
23.
ChengH-YYuCCTsengCCet al. Environment classification and hierarchical lane detection for structured and unstructured roads. IET Comput Vis2010; 4(1): 37–49.
24.
Ei-GindyMMradNTongX. Sensitivity of rearward amplification control of a truck/full trailer to tyre cornering stiffness variations. Proc IMechE, Part D: J Automobile Engineering2001; 215(5): 579–588.
25.
LekkasAMThorIF. Integral LOS path following for curved paths based on a monotone cubic Hermite spline parametrization. IEEE T Contr Syst T2014; 22(6): 2287–2301.
26.
TeeKPShuzhiSGEngHT. Barrier Lyapunov functions for the control of output-constrained nonlinear systems. Automatica2009; 45(4): 918–927.
27.
FilipovAF. Differential equations with discontinuous right-hand side. Providence, RI: American Mathematical Society, 1988.
28.
XianBDawsonDMde QueirozMSet al. A continuous asymptotic tracking control strategy for uncertain nonlinear systems. IEEE T Automat Contr2004; 49(7): 1206–1211.
29.
KendoulF. Nonlinear hierarchical flight controller for unmanned rotorcraft: design, stability, and experiments. J Guid Control Dynam2009; 32(6): 1954–1958.
30.
RenBGeSSTeeKPet al. Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function. IEEE T Neural Networ2010; 21(8): 1339–1345.
31.
KobayashiT. BFO meets HOG: feature extraction based on histograms of oriented p.d.f. gradients for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Portland, OR, 23–28 June 2013. New York: IEEE.
32.
ZhangZ. A flexible new technique for camera calibration. IEEE T Pattern Anal2000; 22(11): 1330–1334.
33.
NietoMSalgadoLJaureguizarFet al. Robust multiple lane road modeling based on perspective analysis. In: 15th IEEE international conference on image processing, San Diego, CA, 12–15 October 2008. New York: IEEE.
PatrikalakisNMTakashiM. Shape interrogation for computer aided design and manufacturing. Berlin: Springer, 2009.
36.
BejiLBestaouiY. Motion generation and adaptive control method of automated guided vehicles in road following. IEEE T Intell Transp2005; 6(1): 113–123.
37.
FreundERobertM. Nonlinear path control in automated vehicle guidance. IEEE T Robotic Autom1997; 13(1): 49–60.
38.
KatriniokAMaschuwJPChristenFet al. Optimal vehicle dynamics control for combined longitudinal and lateral autonomous vehicle guidance. In: European control conference (ECC), Zurich, 17–19 July 2013. New York: IEEE.
39.
WangFChenHCaoD. Nonlinear coordinated motion control of road vehicles after a tire blowout. IEEE T Contr Syst T2016; 24(3): 956–970.
PatrePMMacKunisWKaiserKet al. Asymptotic tracking for uncertain dynamic systems via a multilayer neural network feedforward and RISE feedback control structure. IEEE T Automat Contr2008; 53(9): 2180–2185.
42.
De QueirozMSHuJDawsonDMet al. Adaptive position/force control of robot manipulators without velocity measurements: theory and experimentation. IEEE T Syst Man Cy B1997; 27(5): 796–809.
43.
KimJGYooJHKooJC. Road and lane detection using stereo camera. In: International conference on big data and smart computing (BigComp), Shanghai, China, 15–17 January 2018, pp.649–652. New York: IEEE.
44.
SongMKimCKimMet al. Robust lane tracking algorithm for forward target detection of automated driving vehicles. Proc IMechE, Part D: J Automobile Engineering2019; 233(7): 1930–1949.
45.
ClarkeFH. Optimization and nonsmooth analysis. Philadelphia, PA: Society for Industrial and Applied Mathematics, 1990.
46.
PadenBShankarS. A calculus for computing Filippov’s differential inclusion with application to the variable structure control of robot manipulators. IEEE T Circuits Syst1987; 34(1): 73–82.
47.
FischerNRushikeshKWarrenED. LaSalle-Yoshizawa corollaries for nonsmooth systems. IEEE T Automat Contr2013; 58(9): 2333–2338.
48.
ShevitzDBradP. Lyapunov stability theory of nonsmooth systems. IEEE T Automat Contr1994; 39(9): 1910–1914.
