智能汽车自主循迹控制策略研究
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摘要
智能汽车一直是现代汽车研究领域的热点和难点，伴随着控制理论的发展，越来越多新的控制理论和控制方法被应用于智能汽车的自主循迹控制，这使得如何根据不同的道路环境和行驶工况选择最适合的控制方法成为一门新的课题。本文在研究了智能汽车几种自主循迹横向控制方法和纵向控制方法的基础上，提出了智能汽车自适应径向基函数(Radial Basis Function, RBF)神经网络补偿横向控制方法和两类改进的纵向控制方法，并针对不同的行驶工况提出一种多控制方法变换策略，通过实验验证了控制策略的有效性。
研究了目前国内外智能汽车自主循迹横向控制中常用的汽车转向几何学模型、汽车运动学模型和汽车动力学模型。针对各模型的特点，研究了非预瞄汽车转向几何学模型横向控制方法、基于预瞄的汽车转向几何学模型横向控制方法、汽车运动学模型光滑时变反馈横向控制方法、汽车动力学模型最优线性二次型调节(Linear Quadratic Regulator, LQR)横向控制方法、汽车动力学模型前馈最优LQR横向控制方法和基于预瞄的汽车动力学模型最优LQR横向控制方法，通过双移线仿真试验和圆形弯道仿真试验，从控制器的鲁棒性、对道路的特殊要求、转向超调量、稳态循迹误差以及各自的适宜场合总结了6种方法的优点与不足，为多控制方法的变换奠定了理论基础。
针对名义汽车动力学模型在非线性区失效的问题，提出了智能汽车自主循迹自适应RBF神经网络补偿横向控制方法。分别研究基于模型不确定部分、基于模型整体以及基于模型分块的自适应RBF神经网络补偿方法。使用Lyapunov函数方法保证系统的稳定性，通过双移线仿真试验、圆形弯道仿真试验和连续弯道仿真试验比较3种补偿方法的优劣，最终选择基于模型不确定部分的自适应RBF神经网络补偿控制方法作为智能汽车在非线性区的自主循迹横向控制方法。
在分析了智能汽车4种自主循迹纵向控制模型的基础上，提出2类改进的智能汽车自主循迹纵向控制方法，分别是数据拟合纵向控制方法和模糊神经网络(Fuzzy Neuron Network Control, FNNC)纵向控制方法。仿真试验结果表明，FNNC纵向控制方法在控制精度上要优于数据拟合纵向控制方法，但其输出的期望车速信号变化过于灵敏且存在一定程度的噪声污染。对此，使用db4小波强制阈值降噪方法对FNNC输出的期望车速信号进行处理，取得了良好的效果。
提出了学习向量量化(Learning Vector Quantization, LVQ)神经网络行驶工况分类方法，多组试验结果表明，LVQ神经网络可以对复杂的行驶工况进行有效地分类。控制方法变换策略的理念是针对不同的LVQ行驶工况分类结果，选择与之最适合的控制方法，在保证循迹控制精度的前提下尽可能选择运算量小的控制方法。引入人—车闭环操纵性评价指标对所设计的智能汽车自主循迹控制器的控制效果进行评价，评价结果表明控制器的控制效果良好，但操纵负担略重。
从驾驶模拟实验平台的工作原理、硬件组成和软件设计搭建了驾驶模拟实验平台。借助于CCD图像采集设备和车身状态数据采集设备，将控制器的仿真结果与实车实验数据进行对比，验证了控制器的有效性。
Intelligent vehicle has always been the hotspot and difficulty point in the fieldof modern automobile research. With the development of control theory, more andmore new control theory and control method was applied to path tracking control ofthe intelligent vehicle, which made the question how to choose the most suitablecontrol method according to different road condition and driving condition become anew subject. An intelligent vehicle's lateral control method based on RBF (RadialBasis Function) neuron network compensation and two kinds of improvedlongitudinal control method are proposed based on the research on several commonused methods in intelligent vehicle's path tracking control. Additionally, a controlmethod of switching strategies based on different driving conditions is put forward,whose effectiveness is verified by several experiments.
The geometic steering model, kinematic vehicle model and dynamic vehiclemodel that commanly used in intelligent vehicle's path tracking lateral control arestudied.The non-preview geometric steering model lateral control method, thepreview geometric steering model lateral control method, the kinematic vehiclemodel smooth time-varying feedback lateral control method, the dynamic vehiclemodel based on optimal LQR (Linear Quadratic Regulator) method, the LQRmethod with a feed-forward term and the preview optimal LQR method up to6kinds of common control method in intelligent vehicle's tracking control are studied.Through double lane change test and the circle course test, the respective advantagesand disadvantages of these6methods are studied, including: the controller'srobustness, special requirements of roads, the overshoot, the steady-state lateraltracking error, and their suitable situations, which has been the theoretical basis ofthe multiple control method switching theory.
In order the to solve the lackness of the name dynamic vehicle model innonlinear area, a method based on RBF neural network compensation of theintelligent vehicle's lateral path tracking control is raised.3kinds of RBF networkcompensation methods are studied, separetly based on model uncertain parts, modelpartitioned parts and model united parts. The stability of the system is analyzedbased on lyapunov function method, the advantages and disadvantages of these threekinds of compensation methods are compared through double lane change test,circle course test and a multi curvature road test. The RBF network compensationcontrol method based on model uncertain parts is finally chosen as the lateralcontrol method of an intelligent vehicle in nonlinear area.
Two kinds of intelligent vehicle's longitudinal tracking control methods which separately based on data fitting and FNNC (Fuzzy Neuron Network Control) areproposed on the basis of the analysis of those several common used intelligentvehicle's speed control model. The data analysis shows that the longitudinal controlmethod based on FNNC is superior to longitudinal control method based on datafitting with respected to the control accuracy, but the output desired speed is toosensitive to the of change the input value and there is a certain degree of noisepollution. A method based on wavelet denoising is proposed in order to handle theoutput speed signal of FNNC, which has obtained good effect.
A driving conditions classification method based on LVQ (Learning VectorQuantization) neuron network is raised, several groups of test data show that theLVQ neural network can effectively classify different driving conditions. Thecontrol method switch strategy's theory is selecting different corresponding controlmethods according to different classification results of LVQ, trying to selectmethods with less calculation on the premise of guarantee the tracking controlaccuracy. The driver-vehicle closed-loop maneuverability evaluation index isintroducted in order to evaluate the designed longitudinal and lateral integratedcontroller of the intelligent vehicle's path tracking. According to the evaluation testresults of the double lane change test and the serpentine test, the controller is withfarely good control effectiveness, but also with a heavy burden.
The build process of the driving simulation platformis introduced from theworking theroy, hardware composition and software design aspects. With the aid ofCCD image acquisition equipment and the vehicle state acquisition equipment, theeffect of the controller is veritied through a real vehicle test, which shows that thetest results and simulation results are basically identical.
研究了目前国内外智能汽车自主循迹横向控制中常用的汽车转向几何学模型、汽车运动学模型和汽车动力学模型。针对各模型的特点，研究了非预瞄汽车转向几何学模型横向控制方法、基于预瞄的汽车转向几何学模型横向控制方法、汽车运动学模型光滑时变反馈横向控制方法、汽车动力学模型最优线性二次型调节(Linear Quadratic Regulator, LQR)横向控制方法、汽车动力学模型前馈最优LQR横向控制方法和基于预瞄的汽车动力学模型最优LQR横向控制方法，通过双移线仿真试验和圆形弯道仿真试验，从控制器的鲁棒性、对道路的特殊要求、转向超调量、稳态循迹误差以及各自的适宜场合总结了6种方法的优点与不足，为多控制方法的变换奠定了理论基础。
针对名义汽车动力学模型在非线性区失效的问题，提出了智能汽车自主循迹自适应RBF神经网络补偿横向控制方法。分别研究基于模型不确定部分、基于模型整体以及基于模型分块的自适应RBF神经网络补偿方法。使用Lyapunov函数方法保证系统的稳定性，通过双移线仿真试验、圆形弯道仿真试验和连续弯道仿真试验比较3种补偿方法的优劣，最终选择基于模型不确定部分的自适应RBF神经网络补偿控制方法作为智能汽车在非线性区的自主循迹横向控制方法。
在分析了智能汽车4种自主循迹纵向控制模型的基础上，提出2类改进的智能汽车自主循迹纵向控制方法，分别是数据拟合纵向控制方法和模糊神经网络(Fuzzy Neuron Network Control, FNNC)纵向控制方法。仿真试验结果表明，FNNC纵向控制方法在控制精度上要优于数据拟合纵向控制方法，但其输出的期望车速信号变化过于灵敏且存在一定程度的噪声污染。对此，使用db4小波强制阈值降噪方法对FNNC输出的期望车速信号进行处理，取得了良好的效果。
提出了学习向量量化(Learning Vector Quantization, LVQ)神经网络行驶工况分类方法，多组试验结果表明，LVQ神经网络可以对复杂的行驶工况进行有效地分类。控制方法变换策略的理念是针对不同的LVQ行驶工况分类结果，选择与之最适合的控制方法，在保证循迹控制精度的前提下尽可能选择运算量小的控制方法。引入人—车闭环操纵性评价指标对所设计的智能汽车自主循迹控制器的控制效果进行评价，评价结果表明控制器的控制效果良好，但操纵负担略重。
从驾驶模拟实验平台的工作原理、硬件组成和软件设计搭建了驾驶模拟实验平台。借助于CCD图像采集设备和车身状态数据采集设备，将控制器的仿真结果与实车实验数据进行对比，验证了控制器的有效性。
Intelligent vehicle has always been the hotspot and difficulty point in the fieldof modern automobile research. With the development of control theory, more andmore new control theory and control method was applied to path tracking control ofthe intelligent vehicle, which made the question how to choose the most suitablecontrol method according to different road condition and driving condition become anew subject. An intelligent vehicle's lateral control method based on RBF (RadialBasis Function) neuron network compensation and two kinds of improvedlongitudinal control method are proposed based on the research on several commonused methods in intelligent vehicle's path tracking control. Additionally, a controlmethod of switching strategies based on different driving conditions is put forward,whose effectiveness is verified by several experiments.
The geometic steering model, kinematic vehicle model and dynamic vehiclemodel that commanly used in intelligent vehicle's path tracking lateral control arestudied.The non-preview geometric steering model lateral control method, thepreview geometric steering model lateral control method, the kinematic vehiclemodel smooth time-varying feedback lateral control method, the dynamic vehiclemodel based on optimal LQR (Linear Quadratic Regulator) method, the LQRmethod with a feed-forward term and the preview optimal LQR method up to6kinds of common control method in intelligent vehicle's tracking control are studied.Through double lane change test and the circle course test, the respective advantagesand disadvantages of these6methods are studied, including: the controller'srobustness, special requirements of roads, the overshoot, the steady-state lateraltracking error, and their suitable situations, which has been the theoretical basis ofthe multiple control method switching theory.
In order the to solve the lackness of the name dynamic vehicle model innonlinear area, a method based on RBF neural network compensation of theintelligent vehicle's lateral path tracking control is raised.3kinds of RBF networkcompensation methods are studied, separetly based on model uncertain parts, modelpartitioned parts and model united parts. The stability of the system is analyzedbased on lyapunov function method, the advantages and disadvantages of these threekinds of compensation methods are compared through double lane change test,circle course test and a multi curvature road test. The RBF network compensationcontrol method based on model uncertain parts is finally chosen as the lateralcontrol method of an intelligent vehicle in nonlinear area.
Two kinds of intelligent vehicle's longitudinal tracking control methods which separately based on data fitting and FNNC (Fuzzy Neuron Network Control) areproposed on the basis of the analysis of those several common used intelligentvehicle's speed control model. The data analysis shows that the longitudinal controlmethod based on FNNC is superior to longitudinal control method based on datafitting with respected to the control accuracy, but the output desired speed is toosensitive to the of change the input value and there is a certain degree of noisepollution. A method based on wavelet denoising is proposed in order to handle theoutput speed signal of FNNC, which has obtained good effect.
A driving conditions classification method based on LVQ (Learning VectorQuantization) neuron network is raised, several groups of test data show that theLVQ neural network can effectively classify different driving conditions. Thecontrol method switch strategy's theory is selecting different corresponding controlmethods according to different classification results of LVQ, trying to selectmethods with less calculation on the premise of guarantee the tracking controlaccuracy. The driver-vehicle closed-loop maneuverability evaluation index isintroducted in order to evaluate the designed longitudinal and lateral integratedcontroller of the intelligent vehicle's path tracking. According to the evaluation testresults of the double lane change test and the serpentine test, the controller is withfarely good control effectiveness, but also with a heavy burden.
The build process of the driving simulation platformis introduced from theworking theroy, hardware composition and software design aspects. With the aid ofCCD image acquisition equipment and the vehicle state acquisition equipment, theeffect of the controller is veritied through a real vehicle test, which shows that thetest results and simulation results are basically identical.
引文
[1] Bell, Michael G.H. Presence of Urban ITS Architectures in Europe: Results ofa Recent Survey[J]. Ingegneria Ferroviaria，2012，67(5)：447-467.
[2] Sai, Seii. Current and future ITS[J]. IEICE Tranactions on Information andSystems，2013：176-183.
[3]陈虹,宫洵,胡云峰.汽车控制的研究现状与展望[J].自动化学报，2013，39(4)：322-336.
[4] Zhu Yuan, Xu Xinxi, Xu Youchun. Steering Control Algorithm for IntelligentVehicle Based on Support Vector Machine[J]. Journal of ConvergenceInformation Technology，2012，7(22)：593-599.
[5] Gordon S., Trombly J. Deployment of ITS：A Summary of the2010NationalSurvey Results[C]. FHWA-JPO report，2011：111-132.
[6]钟荣峯.迈向电子化和电动化，智能汽车渐行渐近[J].集成电路应用，2013(3)：40-44.
[7] J. A. Bornstein, C. M. Shoemaker. Army Ground Robotics Research Program[J]. Journal of Ground Vehicle Technology，2003，21(6)：303-310.
[8] R. S. Sharp. Driver Steering Control and a New Perspective on Car HandlingQualities [C]. Journal of Mechanical Engineering，2005，219(C)：1041–1051.
[9] R. Mason, J. Radford, D. Kumar, et al. The Golem Group/UCLA autonomousGround Vehicle in the DARPA Grand Challenge [J]. Journal of FieldRobotics，Special Issue on the DARPA Grand Challenge2005，2005:34-41.
[10] Chris Urmson, Joshua Anhalt, Drew Bagnell, et al. Autonomous Driving inUrban Environments: Boss and the Urban challenge [J]. Journal of FieldRobotics，2008，25(8)：425-466.
[11] Michael Montemerlo, Jan Becker, Suhrid Bhis, et al. Junior: The StanfordEntry in the Urban Challenge [J]. Journal of Field Robotics，2008，25(8)：467-483.
[12] Funke Joseph, Theodosis Paul, Hindiyeh Rami, et al. Up to the Limits:Autonomous Audi TTS[C]. IEEE Intelligent Vehicles Symposium，2012：541-547.
[13] Roglinger Sebastian. A Methodology for Testing Intersection RelatedVehicle-2-X Applications[J]. Computer Networks，2011，55(14)：3154-3168.
[14] Kritayakirana Krisada, Gerdes J. Christian. Autonomous Vehicle Control at theLimits of Handling[J]. International Journal of Vehicle Autonomous Systems，2012，10(4)：271-296.
[15] Faezipour Miad, Nourani Mehrdad, Saeed Adnan, et al. Progress andChallenges in Intelligent Vehicle Area Networks[J]. Communications of theACM，2012，55(2)：90-100.
[16] Frantisek Duchon, Peter Hubinsky, Jaroslav Hanzel. Intelligent Vehicles as theRobotic Applications[J]. Procedia Engineering，2012，48:105-114.
[17] Rob Bakker, Jeroen Hogema, Wim Huiskamp, et al. IRVIN-Intelligent Roadand Vehicle test IN frastructure[C]. Proceedings of the8th International IEEEConference on Intelligent Transportation Systems，Vienna，Austria，2005：947-952.
[18] Broggi Alberto, Cerri Pietro, Felisa Mirko. The Vislab IntercontinentalAutonomous Challenge: An Extensie Test for a Platoon of IntelligentVehicles[J]. International Journal of Vehicle Autonomous Systems，2012，10(3)：147-164.
[19]孙振平,安向京,贺汉根. CITAVT-IV——视觉导航的自主车[J].机器人，2002，24(2)：115-121.
[20]戴斌,聂一鸣,孙振平.地面无人驾驶技术现状及应用[J].汽车与安全，2012，10(3)：46-49.
[21]张朋飞,何克忠,欧阳正柱.多功能室外智能移动机器人实验平台——THMR-V[J].机器人，2002，24(2):97-101.
[22]何克忠.清华智能车技术研究[J].中国新技术新产品，2012(2)：11-14.
[23]李青,郑南宁,马琳等.基于主元神经网络的非结构化道路跟踪[J].机器人，2005，27(3):35-39.
[24]王萍,郭洪艳,陈虹.高速公路车辆智能驾驶仿真平台[J].系统仿真学报学报，2012，24(12)：2473-2478.
[25]陈无畏，李进，王檀彬.视觉导航智能汽车的路径跟踪预瞄控制[J].机械工程学报，2008，44(10)：277-282.
[26]管欣,张立增,贾鑫.驾驶员方向控制弧式预瞄算法的研究[J].汽车工程，2012，34(12)：1061-1065.
[27]郭景华,胡平,李琳辉,等.基于遗传优化的无人车横向模糊控制[J].机械工程学报，2012，48(6)：76-81.
[28]管欣,崔文锋,贾鑫.车辆纵向速度分相控制[J].吉林大学学报（工学版），2013，43(2)：273-277.
[29]人民网.无人驾驶汽车高速试跑抵达天津，超车减速刹车停靠全智能
[EB/OL].(2012-11-25)[2013-1-16].http://news.163.com/12/1125/08/8H55DV8N00014JB6.html.
[30] Jeffrey S.Wit. Vector Pursuit Path Tracking for Autonomous Ground Vehicles
[D]. Florida: University of Florida，2010：13-19.
[31] Sebastian Thrun, Mike Montemerlo, et al. Stanley: The Robot that Won theDARPA Grand Challenge[J]. Journal of Field Robotics，2006，23(9)：661–692.
[32] Paillis, Jean-Luc. Variable Geometric Tracked Unmanned Grounded Vehicle:Model, Stability and Experiments[C].6th International Conference onInformatics in Control，Automation and Robotics，2009：21-28.
[33] Smith, Ryan N. A Geometric Approach to Trajectory Design for anAutonomous Underwater Vehicle: Surveying the Bulbous Bow of a Ship [J].Acta Applicandae Mathematicae，2011：1-24.
[34] Ghosh, Nirmalya. Incremental Unsupervised Three-dimensional VehicleModel Learning From Video[J]. IEEE Transactions on IntelligentTransportation Systems，2010，11(2)：423-440.
[35] Kim, Yongsuk; Jang, Dongyoung. Optimization of Geometric Dimension&Tolerance Parameters of Front Suspension System for Vehicle PullsImprovement [J]. Transactions of the Korean Society of MechanicalEngineers，2009，33(9)：903-912.
[36] Maakaroun, Salim.Geometric Model of a Narrow Tilting CAR Using RoboticsFormalism[C].201015th International Conference on Methods and Models inAutomation and Robotics，2010：377-382.
[37] Unger, Bertram.The Geometric Model for Perceived Roughness Applies toVirtual Textures[C]. Symposium on Haptics Interfaces for VirtualEnvironment and Teleoperator Systems，2008：3-10.
[38] Farmer, Jesse L. Kinematic analysis and design of a two body articulatedrobotic vehicle[C]. Proceedings of the ASME International MechanicalEngineering Congress and Exposition，2010：153-162.
[39] Luo Kai. Nonlinear Time Delay Kinematic Model of UnderwaterManeuverable Supercavitating Vehicles[C]. Proceedings-20102ndInternational Conference on Intelligent Human-Machine Systems andCybernetics，2010：84-88.
[40] Tu Kuo-Yang.A Linear Optimal Tracker Designed for Omnidirectional VehicleDynamics Linearized Based on Kinematic Equations [J]. Robotica，2010：1-11.
[41] Ren Xiaoping. Kinematics Model of Unmanned Driving vehicle[C].Proceedings of the World Congress on Intelligent Control and Automation(WCICA)，2010：5910-5914.
[42] Isaur, Rahman. Kinematics Model for the Wheeled Vehicle TractivePerformance Determination on Sepang's Peat Terrain in Malaysia [J].International Journal of Heavy Vehicle Systems，2009，16(2)：225-242.
[43] Caveney, Derek. Vehicular Path Prediction for Cooperative DrivingApplications Through Digital Map and Dynamic Vehicle Model Fusion[C].Proceedings of the2009IEEE70th Vehicular Technology Conference，2009：32-37.
[44] Zhang, Wei. Longitudinal Dynamic Model of AMT Vehicle for FollowingCondition [J]. Advanced Materials Research，2011，3：403-408.
[45] Han, H.S. Prediction of Ride Quality of a Maglev Vehicle Using a Full VehicleMulti-body Dynamic Model [J]. Vehicle System Dynamics，2009，47(10)：1271-1286.
[46] Zhang, Bing-Qiang. Dynamic Model and Comfortableness Evaluation of aBody-Vehicle-Road Coupled system [J]. Journal of Vibration and Shock，2011，30(1)：1-5.
[47] Hai, Chenguang. A New Wheel Model for Vehicle Dynamic Simulation[C].ICCET2010-2010International Conference on Computer Engineering andTechnology，2010：671-674.
[48] Nair, Ajay. Generic Model of an Electric Vehicle for Dynamic Simulation andPerformance Prediction[C].2010International Conference on ElectricalMachines and Systems，2010：753-757.
[49] Elias K.Xidias, Philip N.Azariadat. Mission Design for a Group ofAutonomous Guided Vehicles [J]. Robotics and Autonomous systems，2011，59(1)：34-43.
[50] Miguel Angel Stelo. Lateral Control Stderivativegy for Autonomous Steeringof Ackerman Like Vehicles [J]. Robotics and Autonomous Systems，2003，45(3)：223-233.
[51] Packiaraj Xavier. A Decentralized PID-based Approach for the CollaborativeDriving of Automated Vehicles [D]. Dalhousie：Dalhousie University.2009：35-42.
[52]王荣本,张荣辉,储江伟.区域交通智能车辆控制器优化设计和品质分析[J].农业机械学报，2007，38(1)：22-25.
[53] Antonella Ferrara, Claudio Vecchio. Second Order Sliding Mode Control ofVehicles with Distributed Collision Avoidance Capabilities [J]. Mechatronics，2009，19：471-477.
[54]李以农,卢少波,杨柳.汽车转向与驱动综合控制及横向速度观测器的设计[J].汽车工程，2007，8(29)：692-697.
[55]李以农,卢少波,郑玲,杨柳.汽车弯道变速行驶时的纵横向耦合控制研究[J].系统仿真学报，2007，19(23)：5524-5528.
[56] Bonilla Isela, Mendoza Marco, et. al. Path-tracking Maneuvers With IndustrialRobot Manipulators Using Uncalibrated Vision and Impedance Control[J].IEEE Transactions on Systems, Man and Cybernetics Part C: Applications andReviews，2012，42(6)：1716-1729.
[57] Perez J., Milanes, V., Godoy J.. Cooperative Controllers for Highways Basedon Human Experience[J]. Expert Systems with Applications，2013，40(4)：1024-1033.
[58]熊波,曲仕茹.基于模糊控制的智能汽车自主行驶方法研究[J].交通运输系统工程与信息，2010，10(2)：70-75.
[59] J. Park, I.W. Sandberg. Universal approximation using radial functionnetworks [J]. Neural Computation,1990，3：246-257.
[60] Cao Z C, Zhao Y T, Wu Q D. Path tracking control for a wheeled mobile robotby integrating neural dynamics with adaptive approach [J]. Control Theory andApplications，2010，27(12)：1717-1223.
[61] Li-Jun Zhang, He-Ming Jia, Xue Qi. NN-adaptive Output Feedback for PathTracking Control of a Surface Ship at High Speed[C]. Proceedings of the48thIEEE Conference on Robtics，2009：2869-2874.
[62] Iguchi,M. A study of Manual Control [J]. Journal of Mechanic Society ofJapan，1959：70-72.
[63] McRuer,D.T.,et al, Theory of Manual Vehicular Control[J]， Ergonomics，1969，12(4)：34-39.
[64]沈峘,谭运生,李舜酩.驾驶员自适应转向控制行为建模[J].农业机械学报，2013，44(2)：12-16.
[65] Fabricio Garelli, Luis Gracia, Antonio Sala, Pedro Albertos. Sliding ModeSpeed Auto-regulation Technique for Robotic Tracking [J]. Robotics andAutonomous Systems，2011，59(7)：519-529.
[66] Bo Xiong, Shiru Qu. Intelligent Vehicle’s Path Tracking Based on fuzzyControl [J]. Journal of Transportation Systems Engineering and InformationTechnology，2010，10(2)：70-75.
[67] Kanarachos Stratis. Design of an Intelligent Feed Forward Controller Systemfor Vehicle Obstacle Avoidance Using Neural Networks[J]. InternationalJournal of Vehicle Systems Modelling and Testing，2013，8(1)：55-87.
[68] Hedrick J.K.Mcmabon D.Narendran V.Swaroop D.Longitudinal VehicleController Design for IVHS System[C]. Proc.of American ControlConference，1991：3107-3110.
[69] Yanakiew Diana,Kanellakopoulos Ioannis. Longitudinal Control of AutomatedCHVS with Significant Actuator Delays [C]. Porc.of the36th IEEEConference on Decision and Control，1997：4756-4763.
[70] Han Keli, Zhu Zhongxiang, Mao Enrong. Joint Control Method of Speed andHeading of Navigation Tractor Based on Optimal Control[J]. Transactions ofthe Chinese Society for Agricultural Machinery，2013，44(2)：165-170.
[71] Prestl, W.The BMW Active Cruise Control ACC [C]. SAE2000-01-0344,SAE Congress，2005：101-104.
[72] Naab, K., Reichart, G.. Driver Assistance Systems for Lateral andLongitudinal Vehicle Guidance-Heading Control and Active Cruise Control
[C], Proc. of AVEC，2004：449-454.
[73] Venhovens, P.J.Th., Bernasch, J.H., L wenau J.P., Rieker, H.G., Schraut, M..The Application of Advanced Vehicle Navigation in BMW Driver AssistanceSystems [C]. SAE Congress，2009：01-49.
[74] Perez J., Milanes V.. Cooperative Controllers for Highways Based on HumanExperience[J]. Expert Systems with Applications，2013，40(4)：1024-1033.
[75] Sei Bum Choi, J.K.Hedrick. Vehicle Longitudinal Control Using an AdaptiveObserver for Automated Highway Systems [C]. In Proc. Of the AmericanControl Conference. Washington，1995：3106-3110.
[76] Kyongsu Yi, et al. Modeling and Control of an Electronic-vacuum Booster forVehicle-to-vehicle Distance control [C]. Proceedings of AVEC，2000：22-24.
[77]姜晓明,王岩,陈兴林.鲁棒迭代学习控制律的优化设计方法[J].电机与控制学报，2013，17(3)：110-116.
[78] D.Swaroop, J.K.Hedrick, C.C.Chien, P.Ioannou. A Comparison of Spacing andHeadway Control Laws for Automatically Controlled Vehicles [J]. VehicleSystem Dynamics，2010，23:597-625.
[79]任殿波,张继业.基于Lyapunov函数方法的时滞汽车纵向跟随控制[J].控制与决策，2007，22(8)：918-921.
[80]李以农,郑玲,谯艳娟.汽车纵向动力学系统的模糊PID控制方法[J].中国机械工程，2006，17(1)：99-103.
[81]李庆中,顾伟康,叶秀清等.移动机器人路径跟踪的智能预瞄控制方法研究[J].机器人，2002，24(3)：252-255.
[82] Kim Wongun，Kim Dongwook, Yi Kyongsu. Development of a Path-trackingControl Systems Based on Model Predictive Control Using InfrastructureSensors[J]. Vehicle System Dynamics，2012，50(6)：1001-1023.
[83] Sahin Cem Safak, Uyar M.Umit. Real-time, Decentralized and Bio-inspiredTopology Control for Holonomic Autonomous Vehicles[J]. InternationalJournal of Intelligent Computing and Cybernetics，2012，5(3)：359-380.
[84] Zhang kun, Cui shengmin. Study on Asymptotic Stability and BoundedStability of the Intelligent Vehicle Platoon [C]. Proceedings of20102nd WRIGlobal Congress on Intelligent Systems，2010:62-65.
[85] Konghui Guo, Feng Pan, Ying Cheng, Haitao Ding. Driver Model Based onthe Preview Optimal Artificial Neural Network[C]. The6th InternationalSymposium on Advanced Vehicle Control (AVEC’02)，2002：401-405.
[86]程颖.方向与速度综合控制驾驶员模型及其在ADAMS中的应用[D].长春：吉林大学车辆工程专业硕士学位论文，2008：24-32.
[87]曹建永.复杂行驶工况下的驾驶员模型[D].长春：吉林大学硕士学位论文，2007：17-23.
[88] A. De Luca, G. Oriolo, C. Samson. Feedback Control of a NonholonomicCar-like Robot[J]. Robot Motion Planning and Control，1998，229：171-249.
[89] R. M. Murray. Control of Nonholonomic Systems using Chained forms [J].Fields Institute Communications，1993：219-245.
[90] Nayl Thaker, Nikolakopoulos George, Gustfsson Thomas. Switching ModelPredictive Control for an Articulated Vehicle Under Varying Slip Angle[C].201220th Mediterrranean Conference on Control and Automation，2012：890-895.
[91] S.L.H. Verhoeven. Optimal Control of a Multi-Actuated By-WireVehicle[D]. Eindhoven: Technische University Eindhoven，2008：8-40.
[92] Jeffrey S.Wit. Vector Pursuit Path Tracking for Autonomous Ground Vehicles
[D]. Florida: University of Florida，2000：45-50.
[93] Paul Yih. Steer-by-Wire: Implications for Vehicle Handling and Safety [D].Stanford：Stanford University，2005：33-39.
[94] Sassano Mario, Astolfi Alessandro. Dynamic Lyapunov functions[J].Automatica，2013，49(4)：1058-1067.
[95] Drury, C.G. Movements with Lateral Constraint [J]. Ergonomics，1971，14(2)：293-305.
[96] Glennon, J.A. Weaver, G.D..The Relationship of Vehicle Paths to HighwayCurve Design [J]. Texas Transportation Institute report，1971：134-135.
[97] Herrin G.D., Neuhardt J.B.. An Empirical Model for Automobile DriverHorizontal Curve Negotiation[J]. Human Factors，1974，16(2)：129-133.
[98] McLean, J.R. Driver Behaviour on Curves-A Review[C]. ARRB Proceedings,1974，7(5)：129-147.
[99] Emmerson J.. Speed of Cars and Sharp Horizontal Curves[J]. TrafficEngineering and Control，1969:135-137.
[100]DeFazio, K. Wittman, D., and Drury, C.G. Effective Vehicle Width in Self-Paced Tracking[J]. Applied Ergonomics，1992，23(6)：382-386.
[101]Bottoms, D.J. The Interaction of Driving Speed, Steering Difficulty andLateral Tolerance with Particular Reference to Agriculture [J]. Ergonomics，1993，26(2)：123-139.
[102]Van Winsum, W., Godthelp, H. Speed Choice and Steering Behaviour inCurve Driving[J]. Human Factors，1996，38(3)：434-441.
[103]Reymond, G. Role of Lateral Acceleration in Curve Driving: Driver Modeland Experiments on a Real Vehicle and a Driving Simulator [J]. HumanFactors，2001，43(3)：483-495.
[104]Jarrod M. Snider. Automatic Steering Methods for Autonomous AutomobilePath Tracking [D]. Pennsylvania: Carnegie Mellon University，2009：25-29.
[105]郭孔辉，丁海涛，宗长富.“人—车”闭环操纵性评价与优化的发展问题[J].机械工程学报，2003，39(10)：27-34.
[106]郭孔辉.汽车操纵动力学原理[M].江苏：科学技术出版社，2011：25-30.
[107]Litkouhi Bakhtiar, Owechko Yuri, Kukshya Vikas. Imaging Sensor Technologyfor Intelligent Vehicle Active Safety and Driver Assistant Systems[J].International Journal of Vehicle Autonomous Systems，2012，10(3)：198-228.
[108]Vu Anh, Ramanandan Arvind, Chen Anning. Real-time ComputerVision/DGPS-aided Inertial Navigation System for Lane-level VehicleNavigation[J]. IEEE Transactions on Intelligent Transportation Systems，2012，13(2)：899-913.
[109]刘富强，田敏，胡振程.智能汽车中基于是觉的道路检测与跟踪算法[J].同济大学学报（自然科学版），2007，35：1535-1537.
[110]Dezhi Gao， Wei Li， Jianmin Duan， Banggui Zheng.A Practical Method ofRoad Detection for Intelligent Vehicle[C]. Proceedings of the IEEEInternational Conference on Automation and Logistics，Beijing，2009：980-985
[111]董瑞先，王玉林，张鲁邹，张亮修.一种基于直线模型的道路识别算法[J].青岛大学学报(工程技术版)，2010，25(1)：18-21.
[112]王科,黄智,钟志华.基于不定Bezier变形模板的城市道路检测算法[J].机械工程学报，2013，49(8)：143-150.
[113]许华荣,王晓栋,方遒.基于B样条曲线模型的结构化道路检测算法[J].自动化学报，2011，37(3)：270-274.
[114]胡晓辉,孙苗强,苏晓许.伪彩色空间完全非结构化道路检测方法[J].中国图像图形学报，2012，17(2)：203-208.
[115]任柯燕.基于机器视觉的室外场景图像理解方法研究[D].北京：北京邮电大学博士学位论文，2012：22-30.
[116]Colace L., Santoni F., Assanto G.. A Near-infrared Optoelectronic Approach toDetection road Conditions[J]. Optics and Lasers in Engineering，2013，51(5)：633-636.
[117]Zhibiao Shu, Konjovic Z., Pap E.. Linear fuzzy Based Road Lane Model andDetection[J]. Knowledge-Based Systems，2013(38)：37-47.
[2] Sai, Seii. Current and future ITS[J]. IEICE Tranactions on Information andSystems，2013：176-183.
[3]陈虹,宫洵,胡云峰.汽车控制的研究现状与展望[J].自动化学报，2013，39(4)：322-336.
[4] Zhu Yuan, Xu Xinxi, Xu Youchun. Steering Control Algorithm for IntelligentVehicle Based on Support Vector Machine[J]. Journal of ConvergenceInformation Technology，2012，7(22)：593-599.
[5] Gordon S., Trombly J. Deployment of ITS：A Summary of the2010NationalSurvey Results[C]. FHWA-JPO report，2011：111-132.
[6]钟荣峯.迈向电子化和电动化，智能汽车渐行渐近[J].集成电路应用，2013(3)：40-44.
[7] J. A. Bornstein, C. M. Shoemaker. Army Ground Robotics Research Program[J]. Journal of Ground Vehicle Technology，2003，21(6)：303-310.
[8] R. S. Sharp. Driver Steering Control and a New Perspective on Car HandlingQualities [C]. Journal of Mechanical Engineering，2005，219(C)：1041–1051.
[9] R. Mason, J. Radford, D. Kumar, et al. The Golem Group/UCLA autonomousGround Vehicle in the DARPA Grand Challenge [J]. Journal of FieldRobotics，Special Issue on the DARPA Grand Challenge2005，2005:34-41.
[10] Chris Urmson, Joshua Anhalt, Drew Bagnell, et al. Autonomous Driving inUrban Environments: Boss and the Urban challenge [J]. Journal of FieldRobotics，2008，25(8)：425-466.
[11] Michael Montemerlo, Jan Becker, Suhrid Bhis, et al. Junior: The StanfordEntry in the Urban Challenge [J]. Journal of Field Robotics，2008，25(8)：467-483.
[12] Funke Joseph, Theodosis Paul, Hindiyeh Rami, et al. Up to the Limits:Autonomous Audi TTS[C]. IEEE Intelligent Vehicles Symposium，2012：541-547.
[13] Roglinger Sebastian. A Methodology for Testing Intersection RelatedVehicle-2-X Applications[J]. Computer Networks，2011，55(14)：3154-3168.
[14] Kritayakirana Krisada, Gerdes J. Christian. Autonomous Vehicle Control at theLimits of Handling[J]. International Journal of Vehicle Autonomous Systems，2012，10(4)：271-296.
[15] Faezipour Miad, Nourani Mehrdad, Saeed Adnan, et al. Progress andChallenges in Intelligent Vehicle Area Networks[J]. Communications of theACM，2012，55(2)：90-100.
[16] Frantisek Duchon, Peter Hubinsky, Jaroslav Hanzel. Intelligent Vehicles as theRobotic Applications[J]. Procedia Engineering，2012，48:105-114.
[17] Rob Bakker, Jeroen Hogema, Wim Huiskamp, et al. IRVIN-Intelligent Roadand Vehicle test IN frastructure[C]. Proceedings of the8th International IEEEConference on Intelligent Transportation Systems，Vienna，Austria，2005：947-952.
[18] Broggi Alberto, Cerri Pietro, Felisa Mirko. The Vislab IntercontinentalAutonomous Challenge: An Extensie Test for a Platoon of IntelligentVehicles[J]. International Journal of Vehicle Autonomous Systems，2012，10(3)：147-164.
[19]孙振平,安向京,贺汉根. CITAVT-IV——视觉导航的自主车[J].机器人，2002，24(2)：115-121.
[20]戴斌,聂一鸣,孙振平.地面无人驾驶技术现状及应用[J].汽车与安全，2012，10(3)：46-49.
[21]张朋飞,何克忠,欧阳正柱.多功能室外智能移动机器人实验平台——THMR-V[J].机器人，2002，24(2):97-101.
[22]何克忠.清华智能车技术研究[J].中国新技术新产品，2012(2)：11-14.
[23]李青,郑南宁,马琳等.基于主元神经网络的非结构化道路跟踪[J].机器人，2005，27(3):35-39.
[24]王萍,郭洪艳,陈虹.高速公路车辆智能驾驶仿真平台[J].系统仿真学报学报，2012，24(12)：2473-2478.
[25]陈无畏，李进，王檀彬.视觉导航智能汽车的路径跟踪预瞄控制[J].机械工程学报，2008，44(10)：277-282.
[26]管欣,张立增,贾鑫.驾驶员方向控制弧式预瞄算法的研究[J].汽车工程，2012，34(12)：1061-1065.
[27]郭景华,胡平,李琳辉,等.基于遗传优化的无人车横向模糊控制[J].机械工程学报，2012，48(6)：76-81.
[28]管欣,崔文锋,贾鑫.车辆纵向速度分相控制[J].吉林大学学报（工学版），2013，43(2)：273-277.
[29]人民网.无人驾驶汽车高速试跑抵达天津，超车减速刹车停靠全智能
[EB/OL].(2012-11-25)[2013-1-16].http://news.163.com/12/1125/08/8H55DV8N00014JB6.html.
[30] Jeffrey S.Wit. Vector Pursuit Path Tracking for Autonomous Ground Vehicles
[D]. Florida: University of Florida，2010：13-19.
[31] Sebastian Thrun, Mike Montemerlo, et al. Stanley: The Robot that Won theDARPA Grand Challenge[J]. Journal of Field Robotics，2006，23(9)：661–692.
[32] Paillis, Jean-Luc. Variable Geometric Tracked Unmanned Grounded Vehicle:Model, Stability and Experiments[C].6th International Conference onInformatics in Control，Automation and Robotics，2009：21-28.
[33] Smith, Ryan N. A Geometric Approach to Trajectory Design for anAutonomous Underwater Vehicle: Surveying the Bulbous Bow of a Ship [J].Acta Applicandae Mathematicae，2011：1-24.
[34] Ghosh, Nirmalya. Incremental Unsupervised Three-dimensional VehicleModel Learning From Video[J]. IEEE Transactions on IntelligentTransportation Systems，2010，11(2)：423-440.
[35] Kim, Yongsuk; Jang, Dongyoung. Optimization of Geometric Dimension&Tolerance Parameters of Front Suspension System for Vehicle PullsImprovement [J]. Transactions of the Korean Society of MechanicalEngineers，2009，33(9)：903-912.
[36] Maakaroun, Salim.Geometric Model of a Narrow Tilting CAR Using RoboticsFormalism[C].201015th International Conference on Methods and Models inAutomation and Robotics，2010：377-382.
[37] Unger, Bertram.The Geometric Model for Perceived Roughness Applies toVirtual Textures[C]. Symposium on Haptics Interfaces for VirtualEnvironment and Teleoperator Systems，2008：3-10.
[38] Farmer, Jesse L. Kinematic analysis and design of a two body articulatedrobotic vehicle[C]. Proceedings of the ASME International MechanicalEngineering Congress and Exposition，2010：153-162.
[39] Luo Kai. Nonlinear Time Delay Kinematic Model of UnderwaterManeuverable Supercavitating Vehicles[C]. Proceedings-20102ndInternational Conference on Intelligent Human-Machine Systems andCybernetics，2010：84-88.
[40] Tu Kuo-Yang.A Linear Optimal Tracker Designed for Omnidirectional VehicleDynamics Linearized Based on Kinematic Equations [J]. Robotica，2010：1-11.
[41] Ren Xiaoping. Kinematics Model of Unmanned Driving vehicle[C].Proceedings of the World Congress on Intelligent Control and Automation(WCICA)，2010：5910-5914.
[42] Isaur, Rahman. Kinematics Model for the Wheeled Vehicle TractivePerformance Determination on Sepang's Peat Terrain in Malaysia [J].International Journal of Heavy Vehicle Systems，2009，16(2)：225-242.
[43] Caveney, Derek. Vehicular Path Prediction for Cooperative DrivingApplications Through Digital Map and Dynamic Vehicle Model Fusion[C].Proceedings of the2009IEEE70th Vehicular Technology Conference，2009：32-37.
[44] Zhang, Wei. Longitudinal Dynamic Model of AMT Vehicle for FollowingCondition [J]. Advanced Materials Research，2011，3：403-408.
[45] Han, H.S. Prediction of Ride Quality of a Maglev Vehicle Using a Full VehicleMulti-body Dynamic Model [J]. Vehicle System Dynamics，2009，47(10)：1271-1286.
[46] Zhang, Bing-Qiang. Dynamic Model and Comfortableness Evaluation of aBody-Vehicle-Road Coupled system [J]. Journal of Vibration and Shock，2011，30(1)：1-5.
[47] Hai, Chenguang. A New Wheel Model for Vehicle Dynamic Simulation[C].ICCET2010-2010International Conference on Computer Engineering andTechnology，2010：671-674.
[48] Nair, Ajay. Generic Model of an Electric Vehicle for Dynamic Simulation andPerformance Prediction[C].2010International Conference on ElectricalMachines and Systems，2010：753-757.
[49] Elias K.Xidias, Philip N.Azariadat. Mission Design for a Group ofAutonomous Guided Vehicles [J]. Robotics and Autonomous systems，2011，59(1)：34-43.
[50] Miguel Angel Stelo. Lateral Control Stderivativegy for Autonomous Steeringof Ackerman Like Vehicles [J]. Robotics and Autonomous Systems，2003，45(3)：223-233.
[51] Packiaraj Xavier. A Decentralized PID-based Approach for the CollaborativeDriving of Automated Vehicles [D]. Dalhousie：Dalhousie University.2009：35-42.
[52]王荣本,张荣辉,储江伟.区域交通智能车辆控制器优化设计和品质分析[J].农业机械学报，2007，38(1)：22-25.
[53] Antonella Ferrara, Claudio Vecchio. Second Order Sliding Mode Control ofVehicles with Distributed Collision Avoidance Capabilities [J]. Mechatronics，2009，19：471-477.
[54]李以农,卢少波,杨柳.汽车转向与驱动综合控制及横向速度观测器的设计[J].汽车工程，2007，8(29)：692-697.
[55]李以农,卢少波,郑玲,杨柳.汽车弯道变速行驶时的纵横向耦合控制研究[J].系统仿真学报，2007，19(23)：5524-5528.
[56] Bonilla Isela, Mendoza Marco, et. al. Path-tracking Maneuvers With IndustrialRobot Manipulators Using Uncalibrated Vision and Impedance Control[J].IEEE Transactions on Systems, Man and Cybernetics Part C: Applications andReviews，2012，42(6)：1716-1729.
[57] Perez J., Milanes, V., Godoy J.. Cooperative Controllers for Highways Basedon Human Experience[J]. Expert Systems with Applications，2013，40(4)：1024-1033.
[58]熊波,曲仕茹.基于模糊控制的智能汽车自主行驶方法研究[J].交通运输系统工程与信息，2010，10(2)：70-75.
[59] J. Park, I.W. Sandberg. Universal approximation using radial functionnetworks [J]. Neural Computation,1990，3：246-257.
[60] Cao Z C, Zhao Y T, Wu Q D. Path tracking control for a wheeled mobile robotby integrating neural dynamics with adaptive approach [J]. Control Theory andApplications，2010，27(12)：1717-1223.
[61] Li-Jun Zhang, He-Ming Jia, Xue Qi. NN-adaptive Output Feedback for PathTracking Control of a Surface Ship at High Speed[C]. Proceedings of the48thIEEE Conference on Robtics，2009：2869-2874.
[62] Iguchi,M. A study of Manual Control [J]. Journal of Mechanic Society ofJapan，1959：70-72.
[63] McRuer,D.T.,et al, Theory of Manual Vehicular Control[J]， Ergonomics，1969，12(4)：34-39.
[64]沈峘,谭运生,李舜酩.驾驶员自适应转向控制行为建模[J].农业机械学报，2013，44(2)：12-16.
[65] Fabricio Garelli, Luis Gracia, Antonio Sala, Pedro Albertos. Sliding ModeSpeed Auto-regulation Technique for Robotic Tracking [J]. Robotics andAutonomous Systems，2011，59(7)：519-529.
[66] Bo Xiong, Shiru Qu. Intelligent Vehicle’s Path Tracking Based on fuzzyControl [J]. Journal of Transportation Systems Engineering and InformationTechnology，2010，10(2)：70-75.
[67] Kanarachos Stratis. Design of an Intelligent Feed Forward Controller Systemfor Vehicle Obstacle Avoidance Using Neural Networks[J]. InternationalJournal of Vehicle Systems Modelling and Testing，2013，8(1)：55-87.
[68] Hedrick J.K.Mcmabon D.Narendran V.Swaroop D.Longitudinal VehicleController Design for IVHS System[C]. Proc.of American ControlConference，1991：3107-3110.
[69] Yanakiew Diana,Kanellakopoulos Ioannis. Longitudinal Control of AutomatedCHVS with Significant Actuator Delays [C]. Porc.of the36th IEEEConference on Decision and Control，1997：4756-4763.
[70] Han Keli, Zhu Zhongxiang, Mao Enrong. Joint Control Method of Speed andHeading of Navigation Tractor Based on Optimal Control[J]. Transactions ofthe Chinese Society for Agricultural Machinery，2013，44(2)：165-170.
[71] Prestl, W.The BMW Active Cruise Control ACC [C]. SAE2000-01-0344,SAE Congress，2005：101-104.
[72] Naab, K., Reichart, G.. Driver Assistance Systems for Lateral andLongitudinal Vehicle Guidance-Heading Control and Active Cruise Control
[C], Proc. of AVEC，2004：449-454.
[73] Venhovens, P.J.Th., Bernasch, J.H., L wenau J.P., Rieker, H.G., Schraut, M..The Application of Advanced Vehicle Navigation in BMW Driver AssistanceSystems [C]. SAE Congress，2009：01-49.
[74] Perez J., Milanes V.. Cooperative Controllers for Highways Based on HumanExperience[J]. Expert Systems with Applications，2013，40(4)：1024-1033.
[75] Sei Bum Choi, J.K.Hedrick. Vehicle Longitudinal Control Using an AdaptiveObserver for Automated Highway Systems [C]. In Proc. Of the AmericanControl Conference. Washington，1995：3106-3110.
[76] Kyongsu Yi, et al. Modeling and Control of an Electronic-vacuum Booster forVehicle-to-vehicle Distance control [C]. Proceedings of AVEC，2000：22-24.
[77]姜晓明,王岩,陈兴林.鲁棒迭代学习控制律的优化设计方法[J].电机与控制学报，2013，17(3)：110-116.
[78] D.Swaroop, J.K.Hedrick, C.C.Chien, P.Ioannou. A Comparison of Spacing andHeadway Control Laws for Automatically Controlled Vehicles [J]. VehicleSystem Dynamics，2010，23:597-625.
[79]任殿波,张继业.基于Lyapunov函数方法的时滞汽车纵向跟随控制[J].控制与决策，2007，22(8)：918-921.
[80]李以农,郑玲,谯艳娟.汽车纵向动力学系统的模糊PID控制方法[J].中国机械工程，2006，17(1)：99-103.
[81]李庆中,顾伟康,叶秀清等.移动机器人路径跟踪的智能预瞄控制方法研究[J].机器人，2002，24(3)：252-255.
[82] Kim Wongun，Kim Dongwook, Yi Kyongsu. Development of a Path-trackingControl Systems Based on Model Predictive Control Using InfrastructureSensors[J]. Vehicle System Dynamics，2012，50(6)：1001-1023.
[83] Sahin Cem Safak, Uyar M.Umit. Real-time, Decentralized and Bio-inspiredTopology Control for Holonomic Autonomous Vehicles[J]. InternationalJournal of Intelligent Computing and Cybernetics，2012，5(3)：359-380.
[84] Zhang kun, Cui shengmin. Study on Asymptotic Stability and BoundedStability of the Intelligent Vehicle Platoon [C]. Proceedings of20102nd WRIGlobal Congress on Intelligent Systems，2010:62-65.
[85] Konghui Guo, Feng Pan, Ying Cheng, Haitao Ding. Driver Model Based onthe Preview Optimal Artificial Neural Network[C]. The6th InternationalSymposium on Advanced Vehicle Control (AVEC’02)，2002：401-405.
[86]程颖.方向与速度综合控制驾驶员模型及其在ADAMS中的应用[D].长春：吉林大学车辆工程专业硕士学位论文，2008：24-32.
[87]曹建永.复杂行驶工况下的驾驶员模型[D].长春：吉林大学硕士学位论文，2007：17-23.
[88] A. De Luca, G. Oriolo, C. Samson. Feedback Control of a NonholonomicCar-like Robot[J]. Robot Motion Planning and Control，1998，229：171-249.
[89] R. M. Murray. Control of Nonholonomic Systems using Chained forms [J].Fields Institute Communications，1993：219-245.
[90] Nayl Thaker, Nikolakopoulos George, Gustfsson Thomas. Switching ModelPredictive Control for an Articulated Vehicle Under Varying Slip Angle[C].201220th Mediterrranean Conference on Control and Automation，2012：890-895.
[91] S.L.H. Verhoeven. Optimal Control of a Multi-Actuated By-WireVehicle[D]. Eindhoven: Technische University Eindhoven，2008：8-40.
[92] Jeffrey S.Wit. Vector Pursuit Path Tracking for Autonomous Ground Vehicles
[D]. Florida: University of Florida，2000：45-50.
[93] Paul Yih. Steer-by-Wire: Implications for Vehicle Handling and Safety [D].Stanford：Stanford University，2005：33-39.
[94] Sassano Mario, Astolfi Alessandro. Dynamic Lyapunov functions[J].Automatica，2013，49(4)：1058-1067.
[95] Drury, C.G. Movements with Lateral Constraint [J]. Ergonomics，1971，14(2)：293-305.
[96] Glennon, J.A. Weaver, G.D..The Relationship of Vehicle Paths to HighwayCurve Design [J]. Texas Transportation Institute report，1971：134-135.
[97] Herrin G.D., Neuhardt J.B.. An Empirical Model for Automobile DriverHorizontal Curve Negotiation[J]. Human Factors，1974，16(2)：129-133.
[98] McLean, J.R. Driver Behaviour on Curves-A Review[C]. ARRB Proceedings,1974，7(5)：129-147.
[99] Emmerson J.. Speed of Cars and Sharp Horizontal Curves[J]. TrafficEngineering and Control，1969:135-137.
[100]DeFazio, K. Wittman, D., and Drury, C.G. Effective Vehicle Width in Self-Paced Tracking[J]. Applied Ergonomics，1992，23(6)：382-386.
[101]Bottoms, D.J. The Interaction of Driving Speed, Steering Difficulty andLateral Tolerance with Particular Reference to Agriculture [J]. Ergonomics，1993，26(2)：123-139.
[102]Van Winsum, W., Godthelp, H. Speed Choice and Steering Behaviour inCurve Driving[J]. Human Factors，1996，38(3)：434-441.
[103]Reymond, G. Role of Lateral Acceleration in Curve Driving: Driver Modeland Experiments on a Real Vehicle and a Driving Simulator [J]. HumanFactors，2001，43(3)：483-495.
[104]Jarrod M. Snider. Automatic Steering Methods for Autonomous AutomobilePath Tracking [D]. Pennsylvania: Carnegie Mellon University，2009：25-29.
[105]郭孔辉，丁海涛，宗长富.“人—车”闭环操纵性评价与优化的发展问题[J].机械工程学报，2003，39(10)：27-34.
[106]郭孔辉.汽车操纵动力学原理[M].江苏：科学技术出版社，2011：25-30.
[107]Litkouhi Bakhtiar, Owechko Yuri, Kukshya Vikas. Imaging Sensor Technologyfor Intelligent Vehicle Active Safety and Driver Assistant Systems[J].International Journal of Vehicle Autonomous Systems，2012，10(3)：198-228.
[108]Vu Anh, Ramanandan Arvind, Chen Anning. Real-time ComputerVision/DGPS-aided Inertial Navigation System for Lane-level VehicleNavigation[J]. IEEE Transactions on Intelligent Transportation Systems，2012，13(2)：899-913.
[109]刘富强，田敏，胡振程.智能汽车中基于是觉的道路检测与跟踪算法[J].同济大学学报（自然科学版），2007，35：1535-1537.
[110]Dezhi Gao， Wei Li， Jianmin Duan， Banggui Zheng.A Practical Method ofRoad Detection for Intelligent Vehicle[C]. Proceedings of the IEEEInternational Conference on Automation and Logistics，Beijing，2009：980-985
[111]董瑞先，王玉林，张鲁邹，张亮修.一种基于直线模型的道路识别算法[J].青岛大学学报(工程技术版)，2010，25(1)：18-21.
[112]王科,黄智,钟志华.基于不定Bezier变形模板的城市道路检测算法[J].机械工程学报，2013，49(8)：143-150.
[113]许华荣,王晓栋,方遒.基于B样条曲线模型的结构化道路检测算法[J].自动化学报，2011，37(3)：270-274.
[114]胡晓辉,孙苗强,苏晓许.伪彩色空间完全非结构化道路检测方法[J].中国图像图形学报，2012，17(2)：203-208.
[115]任柯燕.基于机器视觉的室外场景图像理解方法研究[D].北京：北京邮电大学博士学位论文，2012：22-30.
[116]Colace L., Santoni F., Assanto G.. A Near-infrared Optoelectronic Approach toDetection road Conditions[J]. Optics and Lasers in Engineering，2013，51(5)：633-636.
[117]Zhibiao Shu, Konjovic Z., Pap E.. Linear fuzzy Based Road Lane Model andDetection[J]. Knowledge-Based Systems，2013(38)：37-47.