高速公路环境中自主驾驶车辆运动规划与控制
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摘要
随着车辆密度的增加交通环境日益恶劣,这需要每位驾驶员在驾驶过程中保持高度的注意力来处理可能的交通状况。但是,驾驶员的任何疏忽都可能导致交通事故的发生,从而危及驾乘人员的生命安全。近些年电子、计算机以及控制技术在现代汽车制造业中的不断更新于应用,使得家用汽车在驾乘安全性方面得到了很大提升。尤其是在80年代初诞生的主动安全系统,比如：防抱死制动系统(ABS)、牵引力控制系统(TCS)以及主动前轮转向系统等,大大减少了由于驾驶员的操作失误而引起的车辆事故。这些车辆动力学控制问题所研究的是通过对车辆底层执行机构的动力学分析和控制来满足车辆运行的稳定性要求。在此架构下驾驶员作为决策层给车辆提供行驶策略,而辅助稳定操控系统作为执行层面的稳定控制器,对驾驶员做出的错误或边界驾驶策略进行补偿。这种车辆在某种意义上讲也是一种自主车辆,只不过这种“自主”是在执行层面上的自主,而不是在决策层面上。显然,这种通过执行机构对决策层的错误策略进行补偿的方法可以提高车辆的操纵稳定性,但是这种方法对于车辆的稳定性控制也是有限的。因此,代替驾驶员在决策层面上的规划与控制问题应运而生。
本文的主要内容是自主车辆在高速公路环境中的自主驾驶问题。
为此,本文首先给出了车辆的运动学模型及其推导过程。分析了简化运动学模型以及一般形式运动学模型分别适合的研究范围。进而给出了适合分析平面车辆动力学特性的平面动力学模型。本文后续章节对车辆的规划以及控制方法进行了探讨,所得结论无法在真车环境下进行初步验证。为此,本文需要一个高阶自由度的车辆模型,该模型能够充分反映车辆的关键运动形式。因此,本文建立了用于算法仿真验证的14自由度的车辆动力学模型。详细描述了车辆簧载与非簧载的动力学建模过程。并通过对悬架与车身结合点受力分析的探讨,将悬架模型和车身模型进行了耦合。耦合后的车辆模型减少了代数环环节,提高了仿真速度。进而结合车辆的传动、转向以及制动系统模型在Simulink中完成了14自由度车辆模型的搭建工作。
采用一般形式的运动学模型对给定路径函数形式的车辆轨迹规划问题进行了研究。在对微分平坦基础知识介绍基础上,结合车辆相关动力学特性,给出了一般形式运动学模型的平坦输出表示过程。通过在轨迹规划过程中考虑车辆的侧向加速度约束,使得车辆轨迹满足侧向稳定性要求。这种离线的轨迹规划适合车辆固定路线巡航的需求,而巡航的一个重要指标就是燃油的经济性。为此,本文在对发动机万有特性曲线分析基础上,通过曲线拟合得到车速和油耗的关系函数,并以此作为轨迹规划过程中的性能指标要求。仿真实验结果表明基于微分平坦的轨迹规划方法降低了优化空间的维度,同时其规划轨迹也保证了车辆运行的安全性与经济性。
目前大部分自主车辆都是通过机器视觉对道路信息进行识别,其识别结果通常以点序列的形式给出。要想获得路径函数还需要对路径点进行拟合处理,这无疑增加了处理负担降低了车辆规划的实时性。因此,本文提出了一种基于路径预瞄点信息的路径跟踪方法。由于车辆工作环境为结构化环境,为了给行为决策提供依据,首先对结构化环境进行了详细的数学描述和定义。在此基础上给出了车道可用性与安全性的定义。给出了基于微分平坦的MPC方法描述过程。进而针对不同车行环境下的驾驶行为给出了相应的MPC路径跟踪算法。在对车辆的纵向运动与侧向运动规划控制过程中,考虑了车辆纵向加速度的时变性,并将其作为MPC控制策略的约束项。在14自由度车辆模型上的仿真结果也表明了该方法的可行性与安全性。
无论是自主车辆还是由驾驶员操作的车辆,在高速公路环境中最怕遇到的就是车辆的爆胎。驾驶员由于其反应延迟以及操作失误很容易导致严重的交通事故。同样,自主驾驶车辆如果没有针对爆胎车辆的轮胎特性以及运动特性而设计的控制器,单纯靠普通动力学设计的控制器也无法很好的保持爆胎车辆运动的稳定性。为此,本文描述了车辆爆胎工况下轮胎性能的变化对车辆稳定性的影响。通过对爆胎工况下不同驾驶操作行为的分析和仿真,给出了这种工况下理想的驾驶行为以及安全评价指标。依据该理想驾驶操作及安全指标制定了爆胎工况下基于MPC的规划控制方法。与人类驾驶员爆胎工况下保守的驾驶操作相比,该方法能够明显提升爆胎后车辆的稳定性。
论文对所提出的工况以及所使用的方法进行了明确的论证与仿真,并针对自主车辆的规划以及控制器设计给出了详尽的推导过程。为了验证本文所提出的方法的有效性,设计了几种典型工况以及联合工况下的仿真实验。本文从车辆的系统建模,基于微分平坦的轨迹规划方法介绍,基于微分平坦的MPC方法描述,结构化环境的描述定义以及爆胎车辆的运动特性等方面都给出了详细的分析和讨论。结果表明,文中所提的自主车辆高速公路规划及控制方法效果令人满意。
本文的研究工作也存在一些遗憾,例如在建立14自由度车辆动力学模型过程中忽略了侧向风阻对车辆运动特性的影响,使得模型在分析车辆高速转向过程中会存在建模误差。另外,5自由度车辆动力学模型的平坦输出表示过程也值得进一步研究和探讨。
The increasing traffic density of vehicle needs every motorist to keep high attentive-ness, any inattention may cause problems like automobile accidents and traffic jams which may endanger people's safety in the vehicle. Recent trends in automotive industry point in the direction of increased content of electronics, computers and controls with emphasis on the improved functionality and overall system robustness. Especially the early works on active safety systems date back to the 1980s, such as anti-lock brake systems (ABS), traction control systems (TC) and active front steering systems (AFS), are used to avoid accidents and at the same time facilitate better vehicle controllability and stability. These systems meet the vehicle stability through analyzing and controlling actuators. That is, drivers provide the vehicle with drive strategy and the Intelligent Transportation Systems are only stability controllers which compensate the wrong or critical driving strategies. Such a vehicle is a automotive vehicle in a sense, but it is automotive only on action, not on making decision. Obviously, the method of compensating wrong strategy can improve vehicle stability, but its control capability is limited. Therefore there is growing attention to the planning and active control for automotive vehicle. This thesis mainly studies the autonomous driving in highway for the autonomous vehicles.
Firstly, a kinematic vehicle model is presented. Analyzing the characteristics of simplified vehicle kinematic model and general model, we present a planar dynamic vehicle model adapted to analyzing planar vehicle dynamics characteristics. We also discuss the methods of vehicle path planning, trajectory planning and dynamic control. However, these conclusions can't be validated on actual vehicle. So, this contribution needs a high-level degree vehicle model which can adequately reflect the main movements. For the reason of showing the effectiveness of proposed algorithm, this paper establishes a 14 degrees of freedom vehicle model, which describes the course of dynamic modeling for sprung and unsprung vehicles respectively in detail, and couples the suspension model with vehicle body model through force analysis of point connecting suspension and body. The coupled model reduces algebraic loops and improves the simulation speed. Then we combine vehicle transmission model with steering model and brake model, and establish a 14 degrees of freedom vehicle model in the software of Simulink.
With the usual kinematic model, we discuss the trajectory planning problem for given reference path function. On the basis of introducing differential flatness and vehicle dynamics characteristics, this paper presents the course of usual form of dynamic model. To satisfy the lateral stability, we take the lateral acceleration into account when we plan the trajectory. The trajectory planning off-line method is fit for settled path cruise whose main goal is fuel economy. Therefore, based on the analysis of motors characteristic curve, we fit the curve and get the velocity and fuel function, and make it into a goal function when planning trajectory. Simulation results show that the trajectory planning method based on differential flatness reduces optimization dimensions. At the same time, the trajectory planning method based on differential flatness guarantees the stability and economy.
At present, most automotive vehicles recognize road information through machine vision, and the result is the form of points. In order to obtain the path function, we need to fit the road information points, which doubtlessly increases computation burden and reduces real time of vehicle planning. Therefore, this paper proposes path following method based on path preview points. Due to the environment of vehicle is a structure environment which helps us make decision, we describe and define the structure environ-ment with mathematical language. Also, we define the availability and stability of lane. Then we present the MPC method based on differential flatness. For different operating environments, we propose corresponding MPC path following algorithm. When planning and controlling vehicle motion, we take the longitudinal acceleration and lateral accel-eration into account and make them as the constraints of MPC control method. The simulation results on 14 degrees of freedom vehicle model show availability and stability of our algorithm.
Whether the automotive vehicles or the vehicles operated by drivers are afraid of the tire flat on the highway. The delayed reaction and operated mistakes of the drivers will lead to serious traffic accident. Similarly, if there isn't a controller designed based on tire characteristics and kinetic characteristic of the flat tire vehicle in the automotive vehicle, the designed controller only based on gross dynamics can't ensure the stability of the flat tire vehicle. For this reason, we describe the influence of tire performance changes on the vehicle stability under the condition of vehicle tire flat. Through the simulation and analysis of the different operated behaviors under the condition of tire flat, we present an ideal driving behavior and safety evaluation under this condition, and propose a planning control method under tire flat based on MPC. Compared with the conservative operation of driver under the condition of tire flat, our algorithm can promote the stability of the vehicle obviously under the condition of tire flat.
In this paper, we not only have a series of clear demonstrations and simulations for the working conditions and the methods, but also give the deducing process of the vehicle planning and the controller design in detail. To validate the effectiveness of the proposed methods, we design some simulation experiments under typical and joint working conditions. In this paper, we have a series of detailed analysis and discussion of the vehicle system modeling, the trajectory planning based on flatness, the MPC method based on flatness, the description of the definition of structured environment and the characteristics of the vehicle tire burst. The results show that our planning methods and control methods for automotive vehicle on the highway are satisfactory.
There are some topics that deserve further studying, such as ignoring the influence of the lateral wind on the characteristics of vehicle when we establish the 14 degrees of freedom vehicle dynamic model, so there will be modeling error during high speed turning. In addition, the process which describes the flatness output of the five degrees of freedom vehicle dynamics model is worth researching and discussing in the future.
本文的主要内容是自主车辆在高速公路环境中的自主驾驶问题。
为此,本文首先给出了车辆的运动学模型及其推导过程。分析了简化运动学模型以及一般形式运动学模型分别适合的研究范围。进而给出了适合分析平面车辆动力学特性的平面动力学模型。本文后续章节对车辆的规划以及控制方法进行了探讨,所得结论无法在真车环境下进行初步验证。为此,本文需要一个高阶自由度的车辆模型,该模型能够充分反映车辆的关键运动形式。因此,本文建立了用于算法仿真验证的14自由度的车辆动力学模型。详细描述了车辆簧载与非簧载的动力学建模过程。并通过对悬架与车身结合点受力分析的探讨,将悬架模型和车身模型进行了耦合。耦合后的车辆模型减少了代数环环节,提高了仿真速度。进而结合车辆的传动、转向以及制动系统模型在Simulink中完成了14自由度车辆模型的搭建工作。
采用一般形式的运动学模型对给定路径函数形式的车辆轨迹规划问题进行了研究。在对微分平坦基础知识介绍基础上,结合车辆相关动力学特性,给出了一般形式运动学模型的平坦输出表示过程。通过在轨迹规划过程中考虑车辆的侧向加速度约束,使得车辆轨迹满足侧向稳定性要求。这种离线的轨迹规划适合车辆固定路线巡航的需求,而巡航的一个重要指标就是燃油的经济性。为此,本文在对发动机万有特性曲线分析基础上,通过曲线拟合得到车速和油耗的关系函数,并以此作为轨迹规划过程中的性能指标要求。仿真实验结果表明基于微分平坦的轨迹规划方法降低了优化空间的维度,同时其规划轨迹也保证了车辆运行的安全性与经济性。
目前大部分自主车辆都是通过机器视觉对道路信息进行识别,其识别结果通常以点序列的形式给出。要想获得路径函数还需要对路径点进行拟合处理,这无疑增加了处理负担降低了车辆规划的实时性。因此,本文提出了一种基于路径预瞄点信息的路径跟踪方法。由于车辆工作环境为结构化环境,为了给行为决策提供依据,首先对结构化环境进行了详细的数学描述和定义。在此基础上给出了车道可用性与安全性的定义。给出了基于微分平坦的MPC方法描述过程。进而针对不同车行环境下的驾驶行为给出了相应的MPC路径跟踪算法。在对车辆的纵向运动与侧向运动规划控制过程中,考虑了车辆纵向加速度的时变性,并将其作为MPC控制策略的约束项。在14自由度车辆模型上的仿真结果也表明了该方法的可行性与安全性。
无论是自主车辆还是由驾驶员操作的车辆,在高速公路环境中最怕遇到的就是车辆的爆胎。驾驶员由于其反应延迟以及操作失误很容易导致严重的交通事故。同样,自主驾驶车辆如果没有针对爆胎车辆的轮胎特性以及运动特性而设计的控制器,单纯靠普通动力学设计的控制器也无法很好的保持爆胎车辆运动的稳定性。为此,本文描述了车辆爆胎工况下轮胎性能的变化对车辆稳定性的影响。通过对爆胎工况下不同驾驶操作行为的分析和仿真,给出了这种工况下理想的驾驶行为以及安全评价指标。依据该理想驾驶操作及安全指标制定了爆胎工况下基于MPC的规划控制方法。与人类驾驶员爆胎工况下保守的驾驶操作相比,该方法能够明显提升爆胎后车辆的稳定性。
论文对所提出的工况以及所使用的方法进行了明确的论证与仿真,并针对自主车辆的规划以及控制器设计给出了详尽的推导过程。为了验证本文所提出的方法的有效性,设计了几种典型工况以及联合工况下的仿真实验。本文从车辆的系统建模,基于微分平坦的轨迹规划方法介绍,基于微分平坦的MPC方法描述,结构化环境的描述定义以及爆胎车辆的运动特性等方面都给出了详细的分析和讨论。结果表明,文中所提的自主车辆高速公路规划及控制方法效果令人满意。
本文的研究工作也存在一些遗憾,例如在建立14自由度车辆动力学模型过程中忽略了侧向风阻对车辆运动特性的影响,使得模型在分析车辆高速转向过程中会存在建模误差。另外,5自由度车辆动力学模型的平坦输出表示过程也值得进一步研究和探讨。
The increasing traffic density of vehicle needs every motorist to keep high attentive-ness, any inattention may cause problems like automobile accidents and traffic jams which may endanger people's safety in the vehicle. Recent trends in automotive industry point in the direction of increased content of electronics, computers and controls with emphasis on the improved functionality and overall system robustness. Especially the early works on active safety systems date back to the 1980s, such as anti-lock brake systems (ABS), traction control systems (TC) and active front steering systems (AFS), are used to avoid accidents and at the same time facilitate better vehicle controllability and stability. These systems meet the vehicle stability through analyzing and controlling actuators. That is, drivers provide the vehicle with drive strategy and the Intelligent Transportation Systems are only stability controllers which compensate the wrong or critical driving strategies. Such a vehicle is a automotive vehicle in a sense, but it is automotive only on action, not on making decision. Obviously, the method of compensating wrong strategy can improve vehicle stability, but its control capability is limited. Therefore there is growing attention to the planning and active control for automotive vehicle. This thesis mainly studies the autonomous driving in highway for the autonomous vehicles.
Firstly, a kinematic vehicle model is presented. Analyzing the characteristics of simplified vehicle kinematic model and general model, we present a planar dynamic vehicle model adapted to analyzing planar vehicle dynamics characteristics. We also discuss the methods of vehicle path planning, trajectory planning and dynamic control. However, these conclusions can't be validated on actual vehicle. So, this contribution needs a high-level degree vehicle model which can adequately reflect the main movements. For the reason of showing the effectiveness of proposed algorithm, this paper establishes a 14 degrees of freedom vehicle model, which describes the course of dynamic modeling for sprung and unsprung vehicles respectively in detail, and couples the suspension model with vehicle body model through force analysis of point connecting suspension and body. The coupled model reduces algebraic loops and improves the simulation speed. Then we combine vehicle transmission model with steering model and brake model, and establish a 14 degrees of freedom vehicle model in the software of Simulink.
With the usual kinematic model, we discuss the trajectory planning problem for given reference path function. On the basis of introducing differential flatness and vehicle dynamics characteristics, this paper presents the course of usual form of dynamic model. To satisfy the lateral stability, we take the lateral acceleration into account when we plan the trajectory. The trajectory planning off-line method is fit for settled path cruise whose main goal is fuel economy. Therefore, based on the analysis of motors characteristic curve, we fit the curve and get the velocity and fuel function, and make it into a goal function when planning trajectory. Simulation results show that the trajectory planning method based on differential flatness reduces optimization dimensions. At the same time, the trajectory planning method based on differential flatness guarantees the stability and economy.
At present, most automotive vehicles recognize road information through machine vision, and the result is the form of points. In order to obtain the path function, we need to fit the road information points, which doubtlessly increases computation burden and reduces real time of vehicle planning. Therefore, this paper proposes path following method based on path preview points. Due to the environment of vehicle is a structure environment which helps us make decision, we describe and define the structure environ-ment with mathematical language. Also, we define the availability and stability of lane. Then we present the MPC method based on differential flatness. For different operating environments, we propose corresponding MPC path following algorithm. When planning and controlling vehicle motion, we take the longitudinal acceleration and lateral accel-eration into account and make them as the constraints of MPC control method. The simulation results on 14 degrees of freedom vehicle model show availability and stability of our algorithm.
Whether the automotive vehicles or the vehicles operated by drivers are afraid of the tire flat on the highway. The delayed reaction and operated mistakes of the drivers will lead to serious traffic accident. Similarly, if there isn't a controller designed based on tire characteristics and kinetic characteristic of the flat tire vehicle in the automotive vehicle, the designed controller only based on gross dynamics can't ensure the stability of the flat tire vehicle. For this reason, we describe the influence of tire performance changes on the vehicle stability under the condition of vehicle tire flat. Through the simulation and analysis of the different operated behaviors under the condition of tire flat, we present an ideal driving behavior and safety evaluation under this condition, and propose a planning control method under tire flat based on MPC. Compared with the conservative operation of driver under the condition of tire flat, our algorithm can promote the stability of the vehicle obviously under the condition of tire flat.
In this paper, we not only have a series of clear demonstrations and simulations for the working conditions and the methods, but also give the deducing process of the vehicle planning and the controller design in detail. To validate the effectiveness of the proposed methods, we design some simulation experiments under typical and joint working conditions. In this paper, we have a series of detailed analysis and discussion of the vehicle system modeling, the trajectory planning based on flatness, the MPC method based on flatness, the description of the definition of structured environment and the characteristics of the vehicle tire burst. The results show that our planning methods and control methods for automotive vehicle on the highway are satisfactory.
There are some topics that deserve further studying, such as ignoring the influence of the lateral wind on the characteristics of vehicle when we establish the 14 degrees of freedom vehicle dynamic model, so there will be modeling error during high speed turning. In addition, the process which describes the flatness output of the five degrees of freedom vehicle dynamics model is worth researching and discussing in the future.
引文
[1]钱耀义.汽车发动机电子控制系统[M].北京：机械工业出版社,1999.
[2]程军.汽车防抱死制动系统的理论与实践[M].北京：北京理工大学出版社,1999.
[3]李静,李幼德,赵健.四轮驱动汽车牵引力控制系统研究[J].吉林大学学报(工学版),2003,33(4)：1-6.
[4]陈虹,马苗苗,孙鹏远.基于LMI优化的主动悬架多目标控制[J].自动化学报,2006,32(4)：550-559.
[5]陈虹,马苗苗,孙鹏远.主动悬架H2/广义H2输出反馈控制[J].控制理论与应用,2007,24(5)：790-794.
[6]DIEKMANNS E, MULLER N. Scene Recognition and Navigation Capabilities for Lane Changes and Turns in Vision-Based Vehicle Guidance[J]. Control Engineering Practice,1996,4(5):589-599.
[7]DOUGLAS W G. UGV HISTORY 101:A Brief History of Unmanned Ground Vehicle (UGV) Development Efforts[J]. Unmanned Systems,1995,13:9-32.
[8]SUKTHANKAR R, HANCOCK J, BALUJA S. Adaptive Intelligent Vehicle Modules for Tactical Driving[J]. Proceedings of AAAI-1996 Workshop on Intelligent Adaptive Agents,1996:13-22.
[9]BROGGI A, BERTOZZI M, FASCIOLI A. The ARGO Autonomous Vehicle's Vision and Control Systems [J]. International Journal of Intelligent Control and Systems, 1999:409-441.
[10]孙振平.自主驾驶汽车智能控制系统[M].长沙国防科技大学：博士论文,2004.
[11]IAGNEMMA K, BUEHLER M. Special Issue on the DARPA Grand Challenge, Part 1[J]. Journal of Field Robotics,2006,23(8):461-652.
[12]IAGNEMMA K, BUEHLER M. Special Issue on the DARPA Grand Challenge, Part 2[J]. Journal of Field Robotics,2006,23(9):655-835.
[13]YAMAMURA Y, SETO Y, NISHIRA H. An ACC Design Method for Achieving Both String Stability and Ride Comfort[J]. Journal of System Design and Dynamics, 2008,2(4):979-990.
[14]YI K, HONG J, KWON Y. A Vehicle Control Algorithm for Stop-and-Go Cruise Control[J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering,2001,215(10):1099-1115.
[15]周亮,李克强,连小珉.”起-停”巡航控制系统的纵向车距控制方法[J].清华大学学报(自然科学版),2004,44(8)：1138-1141.
[16]吴沫,安向京,贺汉根.基于视觉的车道跑偏检测方法研究及仿真[J].中国图象图形学报,2007,12(1)：110-115.
[17]PARAG H B. Lane Departure Warning Systems[M]. Robotics Institute, Carnegie Mellon University:PhD Thesis,1999.
[18]MULLER B, DEUTSCHER J, GRODDE S. Trajectory Generation and Feedforward Control for Parking a Car[C]//IEEE International Conference on Control Applica-tions. Erlangen:IEEE Press,2006:163-168.
[19]WU S, HSIN-HAN CHIANG H, PERNG J. Trajectory Generation and Feedfor-ward Control for Parking a Car[C]//Proc. of IEEE Intelligent Vehicles Symposium. Taiwan:IEEE Press,2005:508-513.
[20]王荣本,张明恒,石德乐.双目视觉技术在目标测量中的应用[J].公路交通科技,2007,24(2)：122-125.
[21]安向京.自主驾驶汽车视觉导航系统研究——理论、方法和系统实现[M].长沙国防科技大学：博士论文,2001.
[22]HANCOCK J. Laser Intensity-Based Obstacle Detection and Tracking[M]. Carnegie Mellon University:PhD Thesis,1999.
[23]EWALD A, WILLHOEFT V. Laser Scanners for Obstacle Detection in Automotive Applications[C]//Proceedings of IEEE Intelligent Vehicles Symposium. Hamburg: IEEE Press,2000:682-687.
[24]于春和,陈国斌,刘济林.基于多线激光雷达的障碍检测与环境重建[J].浙江大学学报(工学版),2006,40(6)：1066-1070.
[25]KATO T, NINOMIYA Y, MASAKI I. An Obstacle Detection Method by Fusion of Radar and Motion Stereo [J]. IEEE Transactions on Intelligent Transportation Systems,2002,3(3):182-188.
[26]STEUX B, LAURGEAU C, SALESSE L. A Vehicle Detection and Tracking Sys-tem Featuring Monocular Color Vision and Data Fusion[C]//IEEE Symposium on Intelligent Vehicles. France:IEEE Press,2002:632-639.
[27]SANDOVAL H, HATTORI T, KITAGAWA S. Angle Dependent Edge Detection for Traffic Signs Recognition[C]//Proceedings of the IEEE Intelligent Vehicles Sympo-sium. Tokyo:IEEE Press,2000:308-313.
[28]YANG H, LIU C, LIU K. Traffic Sign Recognition in Disturbing Environments[J]. Foundations of Intelligent Systems,2003,2871:252-261.
[29]ZHU S. The Classification of Traffic Sign Base on Fuzzy Characteristics Training Set[J]. The Fifth World Congress on Intelligent Control and Automation,2004, 6:5266-5270.
[30]蒋刚毅,郑义.基于数学形态学的交通标志自动识别[J].汕头大学学报(自然科学版),1998,13(1)：90-96.
[31]PACLIK P, NOVOVICOVA J, PUDIL P. Road Sign Classification Using Laplace Kernel Classifier [J]. Pattern Recognition Letters,2000,21:1165-1173.
[32]ROMAN C, REINHOLTZ C. Robust Course-boundary Extraction Algorithms for Autonomous Vehicles[J]. IEEE Intelligent Systems and their Applications,1998, 13(6):32-39.
[33]YAMADA M, UEDA K, HORIBA I. Discrimination of the Road Condition Toward Understanding of Vehicle Driving Environments[J]. IEEE Transactions on Intelli-gent Transportation Systems,2001,2(1):26-31.
[34]余卓平,高晓杰.车辆行驶过程中的状态估计问题综述[J].机械工程学报,2009,45(5)：20-33.
[35]魏世孝,周献中.多属性决策理论方法及其在C3I系统中的应用[M].北京：国防工业出版社,1998.
[36]JANSSON J, JOHANSSON J. Decision Making for Collision Avoidance Systems[J]. Society of Automotive Engineers,2002.
[37]邓金城,黄席樾,邓小丽.汽车防撞系统中的危险估计与超车决策[J].计算机仿真,2004,21(11)：207-210.
[38]JULA H, KOSMATOPOULOS E, IOANNOU P. Collision Avoidance Analysis for Lane Changing and Merging[J]. IEEE Transactions on Vehicular Technology,2000, 49(6):2295-2308.
[39]FULLER R. Towards a General Theory of Driver Behaviour[J]. Accident Analysis and Prevention,2005,37:461-472.
[40]LIU Y, OZGUNER U. Human Driver Model and Driver Decision Making for In-tersection Driving[C]//Proceedings of the IEEE Intelligent Vehicles Symposium. Istanbul, Turkey:IEEE Press,2007:642-647.
[41]王晓原,邢丽,吴芳.车道变换决策仿真模型[J].计算机工程与应用,2010,46(16)：216-220.
[42]高振海,管欣,郭孔辉.驾驶员确定汽车预期轨迹的模糊决策模型[J].吉林工业大学自然科学学报,2000,30(1)：7-10.
[43]陈守煜.系统模糊决策理论与应用[M].大连：大连理工大学出版社,1994.
[44]王韦钰,曲仕茹.基于模糊神经网络的自动驾驶决策系统研究[J].计算机测量与控制,2009,17(9)：1711-1713.
[45]王晓原,杨新月.基于决策树的驾驶行为决策机制研究[J].系统仿真学报,2008,20(2)：415-419.
[46]CARSTEN J, RANKIN A, FERGUSON D. Global Path Planning on Board the Mars Exploration Rovers[C]//IEEE Aerospace Conference. Pasadena:IEEE Press, 2007:1-11.
[47]ZHANG Q. A Hierarchical Global Path Planning Approach for AUV Based on Genetic Algorithm[C]//IEEE International Conference on Mechatronics and Au-tomation. Luoyang:IEEE Press,2006:1745-1750.
[48]BELKHOUCHE F. Reactive Path Planning in a Dynamic Environment[J]. IEEE Transactions on Robotics,2009,25(4):902-911.
[49]GE S, LAI X, MAMUN A. Sensor-based Path Planning for Nonholonomic Mobile Robots Subject to Dynamic Constraints[J]. Robotics and Autonomous Systems, 2007,55:513-526.
[50]BOTTASSO C, LEONELLO D, SAVINI B. Path Planning for Autonomous Vehicles by Trajectory Smoothing Using Motion Primitives [J]. IEEE Transactions on Control Systems Technology,2008,16(6):1152-1168.
[51]LOZANO P. Automatic Planning of Manipulator Transfer Movement[J]. IEEE Transactions on Systems, Man and Cybernetics,1981, 11(10):681-698.
[52]LOZANO P. Spatial Planning:A Configuration Space Approach[J]. IEEE Transac-tions on Computers,1983,32(2):108-120.
[53]张钹,张铃.人工智能及其应用丛书：问题求解理论及应用[M].北京：清华大学出版社,1990.
[54]SCHWARTZ J, SHARIR M. On the "Piano Movers" Problem -Ⅱ:General Tech-niques for Computing Topological Properties of Real Algebraic Manifolds[J]. Ad-vances in Applied Mathematics,1983,4:298-351.
[55]LIU Y, OZGUNER U. Optimal Path Planning for Unmanned Air Vehicles with Kinematic and Tactical Constraints[C]//Proceedings of the 41st IEEE Conference on Decision and Control. Brooklyn:IEEE Press,2002:1301-1306.
[56]KHATIB O. Real-time Obstacle Avoidance for Manipulators and Mobile Robots[J]. The International Journal Robotics Research,1986,5(1):90-98.
[57]吴晓涛,孙增圻.用遗传算法进行路径规划[J].清华大学学报(自然科学版),1995,35(5)：14-19.
[58]RYERSON A, ZHANG Q. Vehicle Path Planning for Complete Field Coverage Us-ing Genetic Algorithms[J]. Agricultural Engineering International:CIGR Journal, 2007,4:1-11.
[59]孙增圻.机器人智能控制[M].太原：山西教育出版社,1995.
[60]NELSON H, YE Y. An Intelligent Mobile Vehicle Navigator Based on Fuzzy Logic and Reinforcement Learning[J]. IEEE Transactions on Systems, Man and Cyber-netics,1999,29(2):314-321.
[61]PINCHARD O, LIEGEOIS A, POUGNET F. Generalized Polar Polynomials for Ve-hicle Path Generation with Dynamic Constraints[C]//Proceedings of IEEE Inter-national Conference on Robotics and Automation. Minneapolis, Minnesota:IEEE Press,1996:915-920.
[62]SCHOUWENAARS T, How J, FEPON E. Receding Horizon Path Planning with Implicit Safety Guarantees[C]//Proceedings of the American Control Conference. Boston:[出版者不详],2004:5576-5581.
[63]HIRSCH K, HILGERT J, LALO W. Optimization of Emergency Trajectories for Autonomous Vehicles with Repect to Linear Vehicle Dynamics[C]//Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Mon-terey, USA:IEEE Press,2005:528-533.
[64]HILGERT J, HIRSCH K, BERTRAM T. Emergency Path Planning for Autonomous Vehicles Using Elastic Band Theory[C]//Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Duisburg, Germany:IEEE Press, 2003:1390-1395.
[65]ARNOLD E, NEUPERT J, SAWODNY O. Trajectory Tracking for Boom Cranes Based on Nonlinear Control and Optimal Trajectory Generation[C]//IEEE Con-ference on Control Applications Part of Multi-conference on Systems and Control. Singapore:IEEE Press,2007:1444-1449.
[66]BETTS J. Survey of Numerical Methods for Trajectory Optimization [J]. Journal of Guidance, Control and Dynamics,1998,21(2):193-207.
[67]HARGRAVES C, PARIS S. Direct Trajectory Optimization Using Nonlinear Pro-gramming and Collocation[J]. Journal of Guidance, Control and Dynamics,1987, 10(4):338-342.
[68]HERMAN A, CONWAY B. Direct Optimization Using Collocation Based on High-order Gauss-Lobatto Quadrature Rules[J]. Journal of Guidance, Control and Dy-namics,1996,19(3):592-599.
[69]FARIBA F, ROSS M. Direct Trajectory Optimization by a Chebyshev Pseudospec-tral Method[J]. Journal of Guidance, Control and Dynamics,2002,25(1):160-166.
[70]FARIBA F, Ross M. Legendre Pseudospectral Approximations of Optimal Control Problems [J]. Lecture Notes in Control and Information Sciences,2003,295:327-342.
[71]BHATTACHARYA R, SINGLA P. Nonlinear Trajectory Generation Using Global Local Approximations[C]//Proceedings of IEEE Conference on Decision & Control. San Diego:IEEE Press,2006:6802-6807.
[72]BHATTACHARYA R, BALAS G J, KAYA M A. Nonlinear receding horizon control of an f-16 aircraft[J]. Journal of Guidance, Control and Dynamics,2002,25(5):924-931.
[73]SCHWARTZ A L. Theory and Implementation of Numerical Methods Based on Runge-Kutta Integration for Solving Optimal Control Problems[M]. U.C. Berkeley: PhD Thesis,1996.
[74]VAN NIEUWSTADT M, MURRAY R. Approximate Trajectory Generation for Dif-ferentially Flat Systems with Zero Dynamics [C]//Proceedings of the 34th IEEE Conference on Decision and Control. Pasadena:IEEE Press,1995:4224-4230.
[75]VAN NIEUWSTADT M, MURRAY R. Real Time Trajectory Generation For Differ-entially Flat Systems [J]. International Journal of Robust and Nonlinear Control, 1998,8(11):995-1020.
[76]DONG W, Guo Y. New Trajectory Generation Methods for Nonholonomic Mobile Robots [C]//Proceedings of International Symposium on Collaborative Technologies and Systems. Orlando:IEEE Press,2005:353-358.
[77]Guo Y, TANG T. Optimal Trajectory Generation for Nonholonomic Robots in Dynamic Environments [C]//IEEE International Conference on Robotics and Au-tomation. Pasadena:IEEE Press,2008:2552-2557.
[78]GUO Y, LONG Y, SHENG W. Global Trajectory Generation for Nonholonomic Robots in Dynamic Environments[C]//Proceedings of IEEE International Confer-ence on Robotics and Automation. Hoboken:IEEE Press,2007:1324-1329.
[79]WANG D, QI F. Trajectory Planning for a Four-Wheel-Steering Vehi-cle [C]//Proceedings of the IEEE International Conference on Robotics & Automa-tion. Seoul, Korea:IEEE Press,2001:3320-3325.
[80]LI L, WANG F. Vehicle Trajectory Generation for Optimal Driving Guid-ance[C]//IEEE International Conference on Intelligent Transportation Systems. Singapore:IEEE Press,2002:231-235.
[81]KIM Z, MALIK J. High-quality Vehicle Trajectory Generation from Video Data Based on Vehicle Detection and Description [C]//IEEE International Conference on Intelligent Transportation Systems. CA, USA:IEEE Press,2003:176-182.
[82]FRAZZOLI E, DAHLEN M, FERON E. Robust Hybrid Control for Autonomous Ve-hicle Motion Planning[C]//IEEE Conference on Decision and Control. Cambridge, MA:IEEE Press,2000:821-826.
[83]FRAZZOLI E, DAHLEN M, FERON E. Real-time Trajectory Generation for The Cooperative Path Planning of Multi-vehicle Systems [C]//IEEE Conference on De-cision and Control. Pasadena:IEEE Press,2002:3766-3769.
[84]丛岩峰,陈虹,SAWODNY O.动力学约束下基于微分平坦的车辆轨迹规划[C]／／中国自动化大会暨两化融合高峰会议.浙江：[出版者不详],2009：31-56.
[85]丛岩峰,俞再涛,宋金泽.车辆变动力学约束下基于微分平坦的滚动运动规划[C]//“视听觉信息的认知计算”学术交流会.西安：[出版者不详],2010：236-241.
[86]SHLADOVER S. Review of the State of Development of Advanced Vehicle Control Systems[J]. Vehicle System Dynamics,1995,24:551-595.
[87]ODENTHAL D, BUNTE T, ACKERMANN J. Nonlinear Steering and Braking Con-trol for Vehicle Rollover Avoidance[C]//European Control Conference..[出版地不详]：[出版者不详],1999.
[88]ZHAO S, LI Y, ZHENG L. Vehicle Lateral Stability Control Based on Sliding Mode Control[C]//Proceedings of the IEEE International Conference on Automation and Logistics. Chongqing:IEEE Press,2007:638-642.
[89]BENTON D R.E.AND SMITH. A static-Output-Feedback Design Procedure for Ro-bust Emergency Lateral Control of a Highway Vehicle [J]. IEEE Transactions on Control System Technology,2005,13(4):618-623.
[90]FENTON R, MELOCIK G, OLSON K. On the Steering of Automated Vehicles:The-ory and Experiment[J]. IEEE Transactions on Automatic Control,1976,21(3):306-315.
[91]MACADAM C. Application of an Optimal Preview Control for Simulation of Closed-Loop Automobile Driving[J]. IEEE Transactions on Systems, Man and Cybernetics, 1981, 11(6):393-399.
[92]LIM E, HEDRICK J. Lateral and Longitudinal Vehicle Control Coupling for Au-tomated Vehicle Operation[C]//Proceedings of the American Control Conference. San Diego:IEEE Press,1999:3676-3680.
[93]ARACIL J, HEREDIA G, OLLERO A. Global Stability Analysis of Fuzzy Path Tracking Using Frequency Response [J]. Engineering Applications of Artificial Intel-ligence,2000,13(2):109-119.
[94]HAJJAJI A, BENTALBA S. Fuzzy Path Tracking Control for Automatic Steering of Vehicles [J]. Robotics and Autonomous Systems,2003,43(4):203-213.
[95]BYRNE R H, ABDALLAH C T. Design of a Model Reference Adaptive Con-troller for Vehicle Road Following [J]. Mathematical and Computer Modelling,1995, 22(4):343-354.
[96]YOON Y, SHIN J, KIM H. Model-predictive Active Steering and Obstacle Avoid-ance for Autonomous Ground Vehicles[J]. Control Engineering Practice,2009, 17:741-750.
[97]PAOLO F, FRANCESCO B, JAHAN A. Predictive Active Steering Control for Au-tonomous Vehicle Systems [J]. IEEE Transactions on Control Systems Technology, 2007,15(3):566-580.
[98]CHEN H, ALLGOWER F. A Quasi-infinite Horizon Nonlinear Predictive Control Scheme for Stable Systems:Application to a CSTR[C]//Proc. IFAC ADCHEM. Banff, Canada:[出版者不详],1997：471-476.
[99]CHEN H, ALLGOWER F. A Quasi-infinite Horizon Nonlinear Predictive Control Scheme with Guaranteed Stability[J]. Automatica,1998,34(10):1205-1217.
[100]NAGAI M, HIRANO Y, YAMANAKA S. Integrated Control of Active Rear Wheel Steering and Direct Yaw Moment Control[J]. Vehicle System Dynamics,1997, 27:357-370.
[101]HAC A, BODIE M O. Improvements in Vehicle Handling Through Integrated Control of Chassis System[J]. International Journal of Vehicle Autonomous Systems, 2002, 1(1):83-110.
[102]同济大学理论力学教研室.理论力学[M].上海：同济大学出版社,1997.
[103]MARTIN P, FLIESS M, LEVINE J. Flatness and Defect of Nonlinear Systems:Intro-ductory Theory and Example[J]. International Journal of Control,1995,61(6):1327-1361.
[104]FLIESS M, LEVINE J, MARTIN P. A Lie-backlund Approach to Equivalence and Flatness of Nonlinear Systems[J]. IEEE Transactions on Autonmatic Control,1999, 44(5):922-937.
[105]MURRAY R, RATHINAM M, SLUIS W. Differential Flatness of Mechanical Control Systems:A Catalog of Prototype Systems[C]//Proceedings of the ASME Interna-tional Congress and Exposition. San Francisco, CA:[出版者不详],1995.
[106]MARTIN P, MURRAY R, ROUCHON P. Flat systems[C]//Proceedings of the 4th European Control Conference.[出版地不详]：[出版者不详],1997：211-264.
[107]NIEUWSTADT M, MURRAY R. Approximate Trajectory Generation for Differen-tially Flat Systems with Zero Dynamics[C]//Proceedings of the IEEE Conference on Decision and Control.[出版地不详]：[出版者不详],1995：4224-4230.
[108]QU Z, WANG J, PLAISTED C. A New Analytical Solution to Mobile Robot Tra-jectory Generation in the Presence of Moving Obstacles[J]. IEEE Transactions on Robotics,2004,20(6):978-993.
[109]ANTRITTER F. On the Relations Between Different Flatness Based Design Methods for Tracking Controllers[C]//Proceedings of the IEEE Conference on Decision and Control. Munchen:[出版者不详],2008：1740-1745.
[110]解学书.最优控制理论与应用[M].北京：清华大学出版社,1986.
[111]LEWIS F, SYRMOS V. Optimal Control[M]. USA:John Wiley & Sons, Inc.,1995.
[112]PACEJKA H B, BAKKER E. The Magic Formula Tyre Model[J]. Vehicle System Dynamics,1991,21(5):1-18.
[113]PACEJKA H B, BAKKER E. Magic Formula Tyre Model with Transient Proper-ties[J]. Vehicle System Dynamics,1997,27(18):234-249.
[114]郭孔辉.汽车操纵动力学[M].吉林：吉林科学技术出版社,1991.
[115]郭孔辉.轮胎侧偏特性的一般理论模型[J].汽车工程,1990,12(3)：1-12.
[116]郭孔辉.各向摩擦系数不同条件下轮胎力学特性的统一理论模型[J].中国机械工程,1996,7(4)：90-93.
[117]Guo K H. A Unified Non-Steady Non-Linear Tyre Model Under Complex Wheel Motion Inputs Including Extreme Operating Cconditions[J]. JSAE Review,2001, 22(4):395-402.
[118]丁海涛.轮胎附着极限下汽车稳定性控制的仿真研究[M].吉林大学：博士论文,2003.
[119]杨得军,林柏忠,管欣.用于开发型驾驶模拟器的17自由度汽车动力学仿真的数学模型及算法[J].汽车技术,2002,12：6-9.
[120]SAYERS M, HAN D. A Generic Multibody Vehicle Model for Simulating Handling and Braking[J]. Vehicle System Dynamics,1996,25:599-613.
[121]RAJAMANI R. Vehicle Dynamics and Control[M]. USA:Springer,2006.
[122]GILLESPIE T. Fundamentals of Vehicle Dynamics[M]. USA:SAE International, 1992.
[123]席裕庚,耿晓军,陈虹.预测控制性能研究的新进展[J].控制理论与应用,2000,17(4)：469-475.
[124]GARCIA C, PRETT D, MORARI M. Model Predictive Control:Theory and Practice —a Survey[J]. Automatica,1989,25:315-348.
[125]陈然.汽车轮胎监测及控制系统的研究[M].重庆大学：硕士论文,2005.
[126]曹兴举.汽车在高速公路上爆胎原因及防治措施[J].交通科技与经济,2008,2：56-57.
[127]纵滔,高进东,程飞.西宝高速公路交通事故分析[J].交通医学,2001,15(6)：593-595.
[128]傅建中,石勇.轮胎气压检测与爆胎自动减速系统[J].汽车工程,2006,28(2)：199-202.
[129]王英麟.基于CarSim与UniTire的爆胎汽车动力学响应研究[M].吉林大学：硕士论文,2006.
[130]黄江,郭孔辉,宋晓琳. 爆胎汽车的稳定性控制[J]. 中国机械工程,2009,20(16)：2006-2010.
[131]RIEKERT P, SCHUNK T. Zur Fahrmechanik des gummibereiften Kraftfahrzeugs[J]. Ingenieur Archiv,1940,11:210-224.
[2]程军.汽车防抱死制动系统的理论与实践[M].北京：北京理工大学出版社,1999.
[3]李静,李幼德,赵健.四轮驱动汽车牵引力控制系统研究[J].吉林大学学报(工学版),2003,33(4)：1-6.
[4]陈虹,马苗苗,孙鹏远.基于LMI优化的主动悬架多目标控制[J].自动化学报,2006,32(4)：550-559.
[5]陈虹,马苗苗,孙鹏远.主动悬架H2/广义H2输出反馈控制[J].控制理论与应用,2007,24(5)：790-794.
[6]DIEKMANNS E, MULLER N. Scene Recognition and Navigation Capabilities for Lane Changes and Turns in Vision-Based Vehicle Guidance[J]. Control Engineering Practice,1996,4(5):589-599.
[7]DOUGLAS W G. UGV HISTORY 101:A Brief History of Unmanned Ground Vehicle (UGV) Development Efforts[J]. Unmanned Systems,1995,13:9-32.
[8]SUKTHANKAR R, HANCOCK J, BALUJA S. Adaptive Intelligent Vehicle Modules for Tactical Driving[J]. Proceedings of AAAI-1996 Workshop on Intelligent Adaptive Agents,1996:13-22.
[9]BROGGI A, BERTOZZI M, FASCIOLI A. The ARGO Autonomous Vehicle's Vision and Control Systems [J]. International Journal of Intelligent Control and Systems, 1999:409-441.
[10]孙振平.自主驾驶汽车智能控制系统[M].长沙国防科技大学：博士论文,2004.
[11]IAGNEMMA K, BUEHLER M. Special Issue on the DARPA Grand Challenge, Part 1[J]. Journal of Field Robotics,2006,23(8):461-652.
[12]IAGNEMMA K, BUEHLER M. Special Issue on the DARPA Grand Challenge, Part 2[J]. Journal of Field Robotics,2006,23(9):655-835.
[13]YAMAMURA Y, SETO Y, NISHIRA H. An ACC Design Method for Achieving Both String Stability and Ride Comfort[J]. Journal of System Design and Dynamics, 2008,2(4):979-990.
[14]YI K, HONG J, KWON Y. A Vehicle Control Algorithm for Stop-and-Go Cruise Control[J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering,2001,215(10):1099-1115.
[15]周亮,李克强,连小珉.”起-停”巡航控制系统的纵向车距控制方法[J].清华大学学报(自然科学版),2004,44(8)：1138-1141.
[16]吴沫,安向京,贺汉根.基于视觉的车道跑偏检测方法研究及仿真[J].中国图象图形学报,2007,12(1)：110-115.
[17]PARAG H B. Lane Departure Warning Systems[M]. Robotics Institute, Carnegie Mellon University:PhD Thesis,1999.
[18]MULLER B, DEUTSCHER J, GRODDE S. Trajectory Generation and Feedforward Control for Parking a Car[C]//IEEE International Conference on Control Applica-tions. Erlangen:IEEE Press,2006:163-168.
[19]WU S, HSIN-HAN CHIANG H, PERNG J. Trajectory Generation and Feedfor-ward Control for Parking a Car[C]//Proc. of IEEE Intelligent Vehicles Symposium. Taiwan:IEEE Press,2005:508-513.
[20]王荣本,张明恒,石德乐.双目视觉技术在目标测量中的应用[J].公路交通科技,2007,24(2)：122-125.
[21]安向京.自主驾驶汽车视觉导航系统研究——理论、方法和系统实现[M].长沙国防科技大学：博士论文,2001.
[22]HANCOCK J. Laser Intensity-Based Obstacle Detection and Tracking[M]. Carnegie Mellon University:PhD Thesis,1999.
[23]EWALD A, WILLHOEFT V. Laser Scanners for Obstacle Detection in Automotive Applications[C]//Proceedings of IEEE Intelligent Vehicles Symposium. Hamburg: IEEE Press,2000:682-687.
[24]于春和,陈国斌,刘济林.基于多线激光雷达的障碍检测与环境重建[J].浙江大学学报(工学版),2006,40(6)：1066-1070.
[25]KATO T, NINOMIYA Y, MASAKI I. An Obstacle Detection Method by Fusion of Radar and Motion Stereo [J]. IEEE Transactions on Intelligent Transportation Systems,2002,3(3):182-188.
[26]STEUX B, LAURGEAU C, SALESSE L. A Vehicle Detection and Tracking Sys-tem Featuring Monocular Color Vision and Data Fusion[C]//IEEE Symposium on Intelligent Vehicles. France:IEEE Press,2002:632-639.
[27]SANDOVAL H, HATTORI T, KITAGAWA S. Angle Dependent Edge Detection for Traffic Signs Recognition[C]//Proceedings of the IEEE Intelligent Vehicles Sympo-sium. Tokyo:IEEE Press,2000:308-313.
[28]YANG H, LIU C, LIU K. Traffic Sign Recognition in Disturbing Environments[J]. Foundations of Intelligent Systems,2003,2871:252-261.
[29]ZHU S. The Classification of Traffic Sign Base on Fuzzy Characteristics Training Set[J]. The Fifth World Congress on Intelligent Control and Automation,2004, 6:5266-5270.
[30]蒋刚毅,郑义.基于数学形态学的交通标志自动识别[J].汕头大学学报(自然科学版),1998,13(1)：90-96.
[31]PACLIK P, NOVOVICOVA J, PUDIL P. Road Sign Classification Using Laplace Kernel Classifier [J]. Pattern Recognition Letters,2000,21:1165-1173.
[32]ROMAN C, REINHOLTZ C. Robust Course-boundary Extraction Algorithms for Autonomous Vehicles[J]. IEEE Intelligent Systems and their Applications,1998, 13(6):32-39.
[33]YAMADA M, UEDA K, HORIBA I. Discrimination of the Road Condition Toward Understanding of Vehicle Driving Environments[J]. IEEE Transactions on Intelli-gent Transportation Systems,2001,2(1):26-31.
[34]余卓平,高晓杰.车辆行驶过程中的状态估计问题综述[J].机械工程学报,2009,45(5)：20-33.
[35]魏世孝,周献中.多属性决策理论方法及其在C3I系统中的应用[M].北京：国防工业出版社,1998.
[36]JANSSON J, JOHANSSON J. Decision Making for Collision Avoidance Systems[J]. Society of Automotive Engineers,2002.
[37]邓金城,黄席樾,邓小丽.汽车防撞系统中的危险估计与超车决策[J].计算机仿真,2004,21(11)：207-210.
[38]JULA H, KOSMATOPOULOS E, IOANNOU P. Collision Avoidance Analysis for Lane Changing and Merging[J]. IEEE Transactions on Vehicular Technology,2000, 49(6):2295-2308.
[39]FULLER R. Towards a General Theory of Driver Behaviour[J]. Accident Analysis and Prevention,2005,37:461-472.
[40]LIU Y, OZGUNER U. Human Driver Model and Driver Decision Making for In-tersection Driving[C]//Proceedings of the IEEE Intelligent Vehicles Symposium. Istanbul, Turkey:IEEE Press,2007:642-647.
[41]王晓原,邢丽,吴芳.车道变换决策仿真模型[J].计算机工程与应用,2010,46(16)：216-220.
[42]高振海,管欣,郭孔辉.驾驶员确定汽车预期轨迹的模糊决策模型[J].吉林工业大学自然科学学报,2000,30(1)：7-10.
[43]陈守煜.系统模糊决策理论与应用[M].大连：大连理工大学出版社,1994.
[44]王韦钰,曲仕茹.基于模糊神经网络的自动驾驶决策系统研究[J].计算机测量与控制,2009,17(9)：1711-1713.
[45]王晓原,杨新月.基于决策树的驾驶行为决策机制研究[J].系统仿真学报,2008,20(2)：415-419.
[46]CARSTEN J, RANKIN A, FERGUSON D. Global Path Planning on Board the Mars Exploration Rovers[C]//IEEE Aerospace Conference. Pasadena:IEEE Press, 2007:1-11.
[47]ZHANG Q. A Hierarchical Global Path Planning Approach for AUV Based on Genetic Algorithm[C]//IEEE International Conference on Mechatronics and Au-tomation. Luoyang:IEEE Press,2006:1745-1750.
[48]BELKHOUCHE F. Reactive Path Planning in a Dynamic Environment[J]. IEEE Transactions on Robotics,2009,25(4):902-911.
[49]GE S, LAI X, MAMUN A. Sensor-based Path Planning for Nonholonomic Mobile Robots Subject to Dynamic Constraints[J]. Robotics and Autonomous Systems, 2007,55:513-526.
[50]BOTTASSO C, LEONELLO D, SAVINI B. Path Planning for Autonomous Vehicles by Trajectory Smoothing Using Motion Primitives [J]. IEEE Transactions on Control Systems Technology,2008,16(6):1152-1168.
[51]LOZANO P. Automatic Planning of Manipulator Transfer Movement[J]. IEEE Transactions on Systems, Man and Cybernetics,1981, 11(10):681-698.
[52]LOZANO P. Spatial Planning:A Configuration Space Approach[J]. IEEE Transac-tions on Computers,1983,32(2):108-120.
[53]张钹,张铃.人工智能及其应用丛书：问题求解理论及应用[M].北京：清华大学出版社,1990.
[54]SCHWARTZ J, SHARIR M. On the "Piano Movers" Problem -Ⅱ:General Tech-niques for Computing Topological Properties of Real Algebraic Manifolds[J]. Ad-vances in Applied Mathematics,1983,4:298-351.
[55]LIU Y, OZGUNER U. Optimal Path Planning for Unmanned Air Vehicles with Kinematic and Tactical Constraints[C]//Proceedings of the 41st IEEE Conference on Decision and Control. Brooklyn:IEEE Press,2002:1301-1306.
[56]KHATIB O. Real-time Obstacle Avoidance for Manipulators and Mobile Robots[J]. The International Journal Robotics Research,1986,5(1):90-98.
[57]吴晓涛,孙增圻.用遗传算法进行路径规划[J].清华大学学报(自然科学版),1995,35(5)：14-19.
[58]RYERSON A, ZHANG Q. Vehicle Path Planning for Complete Field Coverage Us-ing Genetic Algorithms[J]. Agricultural Engineering International:CIGR Journal, 2007,4:1-11.
[59]孙增圻.机器人智能控制[M].太原：山西教育出版社,1995.
[60]NELSON H, YE Y. An Intelligent Mobile Vehicle Navigator Based on Fuzzy Logic and Reinforcement Learning[J]. IEEE Transactions on Systems, Man and Cyber-netics,1999,29(2):314-321.
[61]PINCHARD O, LIEGEOIS A, POUGNET F. Generalized Polar Polynomials for Ve-hicle Path Generation with Dynamic Constraints[C]//Proceedings of IEEE Inter-national Conference on Robotics and Automation. Minneapolis, Minnesota:IEEE Press,1996:915-920.
[62]SCHOUWENAARS T, How J, FEPON E. Receding Horizon Path Planning with Implicit Safety Guarantees[C]//Proceedings of the American Control Conference. Boston:[出版者不详],2004:5576-5581.
[63]HIRSCH K, HILGERT J, LALO W. Optimization of Emergency Trajectories for Autonomous Vehicles with Repect to Linear Vehicle Dynamics[C]//Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Mon-terey, USA:IEEE Press,2005:528-533.
[64]HILGERT J, HIRSCH K, BERTRAM T. Emergency Path Planning for Autonomous Vehicles Using Elastic Band Theory[C]//Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Duisburg, Germany:IEEE Press, 2003:1390-1395.
[65]ARNOLD E, NEUPERT J, SAWODNY O. Trajectory Tracking for Boom Cranes Based on Nonlinear Control and Optimal Trajectory Generation[C]//IEEE Con-ference on Control Applications Part of Multi-conference on Systems and Control. Singapore:IEEE Press,2007:1444-1449.
[66]BETTS J. Survey of Numerical Methods for Trajectory Optimization [J]. Journal of Guidance, Control and Dynamics,1998,21(2):193-207.
[67]HARGRAVES C, PARIS S. Direct Trajectory Optimization Using Nonlinear Pro-gramming and Collocation[J]. Journal of Guidance, Control and Dynamics,1987, 10(4):338-342.
[68]HERMAN A, CONWAY B. Direct Optimization Using Collocation Based on High-order Gauss-Lobatto Quadrature Rules[J]. Journal of Guidance, Control and Dy-namics,1996,19(3):592-599.
[69]FARIBA F, ROSS M. Direct Trajectory Optimization by a Chebyshev Pseudospec-tral Method[J]. Journal of Guidance, Control and Dynamics,2002,25(1):160-166.
[70]FARIBA F, Ross M. Legendre Pseudospectral Approximations of Optimal Control Problems [J]. Lecture Notes in Control and Information Sciences,2003,295:327-342.
[71]BHATTACHARYA R, SINGLA P. Nonlinear Trajectory Generation Using Global Local Approximations[C]//Proceedings of IEEE Conference on Decision & Control. San Diego:IEEE Press,2006:6802-6807.
[72]BHATTACHARYA R, BALAS G J, KAYA M A. Nonlinear receding horizon control of an f-16 aircraft[J]. Journal of Guidance, Control and Dynamics,2002,25(5):924-931.
[73]SCHWARTZ A L. Theory and Implementation of Numerical Methods Based on Runge-Kutta Integration for Solving Optimal Control Problems[M]. U.C. Berkeley: PhD Thesis,1996.
[74]VAN NIEUWSTADT M, MURRAY R. Approximate Trajectory Generation for Dif-ferentially Flat Systems with Zero Dynamics [C]//Proceedings of the 34th IEEE Conference on Decision and Control. Pasadena:IEEE Press,1995:4224-4230.
[75]VAN NIEUWSTADT M, MURRAY R. Real Time Trajectory Generation For Differ-entially Flat Systems [J]. International Journal of Robust and Nonlinear Control, 1998,8(11):995-1020.
[76]DONG W, Guo Y. New Trajectory Generation Methods for Nonholonomic Mobile Robots [C]//Proceedings of International Symposium on Collaborative Technologies and Systems. Orlando:IEEE Press,2005:353-358.
[77]Guo Y, TANG T. Optimal Trajectory Generation for Nonholonomic Robots in Dynamic Environments [C]//IEEE International Conference on Robotics and Au-tomation. Pasadena:IEEE Press,2008:2552-2557.
[78]GUO Y, LONG Y, SHENG W. Global Trajectory Generation for Nonholonomic Robots in Dynamic Environments[C]//Proceedings of IEEE International Confer-ence on Robotics and Automation. Hoboken:IEEE Press,2007:1324-1329.
[79]WANG D, QI F. Trajectory Planning for a Four-Wheel-Steering Vehi-cle [C]//Proceedings of the IEEE International Conference on Robotics & Automa-tion. Seoul, Korea:IEEE Press,2001:3320-3325.
[80]LI L, WANG F. Vehicle Trajectory Generation for Optimal Driving Guid-ance[C]//IEEE International Conference on Intelligent Transportation Systems. Singapore:IEEE Press,2002:231-235.
[81]KIM Z, MALIK J. High-quality Vehicle Trajectory Generation from Video Data Based on Vehicle Detection and Description [C]//IEEE International Conference on Intelligent Transportation Systems. CA, USA:IEEE Press,2003:176-182.
[82]FRAZZOLI E, DAHLEN M, FERON E. Robust Hybrid Control for Autonomous Ve-hicle Motion Planning[C]//IEEE Conference on Decision and Control. Cambridge, MA:IEEE Press,2000:821-826.
[83]FRAZZOLI E, DAHLEN M, FERON E. Real-time Trajectory Generation for The Cooperative Path Planning of Multi-vehicle Systems [C]//IEEE Conference on De-cision and Control. Pasadena:IEEE Press,2002:3766-3769.
[84]丛岩峰,陈虹,SAWODNY O.动力学约束下基于微分平坦的车辆轨迹规划[C]／／中国自动化大会暨两化融合高峰会议.浙江：[出版者不详],2009：31-56.
[85]丛岩峰,俞再涛,宋金泽.车辆变动力学约束下基于微分平坦的滚动运动规划[C]//“视听觉信息的认知计算”学术交流会.西安：[出版者不详],2010：236-241.
[86]SHLADOVER S. Review of the State of Development of Advanced Vehicle Control Systems[J]. Vehicle System Dynamics,1995,24:551-595.
[87]ODENTHAL D, BUNTE T, ACKERMANN J. Nonlinear Steering and Braking Con-trol for Vehicle Rollover Avoidance[C]//European Control Conference..[出版地不详]：[出版者不详],1999.
[88]ZHAO S, LI Y, ZHENG L. Vehicle Lateral Stability Control Based on Sliding Mode Control[C]//Proceedings of the IEEE International Conference on Automation and Logistics. Chongqing:IEEE Press,2007:638-642.
[89]BENTON D R.E.AND SMITH. A static-Output-Feedback Design Procedure for Ro-bust Emergency Lateral Control of a Highway Vehicle [J]. IEEE Transactions on Control System Technology,2005,13(4):618-623.
[90]FENTON R, MELOCIK G, OLSON K. On the Steering of Automated Vehicles:The-ory and Experiment[J]. IEEE Transactions on Automatic Control,1976,21(3):306-315.
[91]MACADAM C. Application of an Optimal Preview Control for Simulation of Closed-Loop Automobile Driving[J]. IEEE Transactions on Systems, Man and Cybernetics, 1981, 11(6):393-399.
[92]LIM E, HEDRICK J. Lateral and Longitudinal Vehicle Control Coupling for Au-tomated Vehicle Operation[C]//Proceedings of the American Control Conference. San Diego:IEEE Press,1999:3676-3680.
[93]ARACIL J, HEREDIA G, OLLERO A. Global Stability Analysis of Fuzzy Path Tracking Using Frequency Response [J]. Engineering Applications of Artificial Intel-ligence,2000,13(2):109-119.
[94]HAJJAJI A, BENTALBA S. Fuzzy Path Tracking Control for Automatic Steering of Vehicles [J]. Robotics and Autonomous Systems,2003,43(4):203-213.
[95]BYRNE R H, ABDALLAH C T. Design of a Model Reference Adaptive Con-troller for Vehicle Road Following [J]. Mathematical and Computer Modelling,1995, 22(4):343-354.
[96]YOON Y, SHIN J, KIM H. Model-predictive Active Steering and Obstacle Avoid-ance for Autonomous Ground Vehicles[J]. Control Engineering Practice,2009, 17:741-750.
[97]PAOLO F, FRANCESCO B, JAHAN A. Predictive Active Steering Control for Au-tonomous Vehicle Systems [J]. IEEE Transactions on Control Systems Technology, 2007,15(3):566-580.
[98]CHEN H, ALLGOWER F. A Quasi-infinite Horizon Nonlinear Predictive Control Scheme for Stable Systems:Application to a CSTR[C]//Proc. IFAC ADCHEM. Banff, Canada:[出版者不详],1997：471-476.
[99]CHEN H, ALLGOWER F. A Quasi-infinite Horizon Nonlinear Predictive Control Scheme with Guaranteed Stability[J]. Automatica,1998,34(10):1205-1217.
[100]NAGAI M, HIRANO Y, YAMANAKA S. Integrated Control of Active Rear Wheel Steering and Direct Yaw Moment Control[J]. Vehicle System Dynamics,1997, 27:357-370.
[101]HAC A, BODIE M O. Improvements in Vehicle Handling Through Integrated Control of Chassis System[J]. International Journal of Vehicle Autonomous Systems, 2002, 1(1):83-110.
[102]同济大学理论力学教研室.理论力学[M].上海：同济大学出版社,1997.
[103]MARTIN P, FLIESS M, LEVINE J. Flatness and Defect of Nonlinear Systems:Intro-ductory Theory and Example[J]. International Journal of Control,1995,61(6):1327-1361.
[104]FLIESS M, LEVINE J, MARTIN P. A Lie-backlund Approach to Equivalence and Flatness of Nonlinear Systems[J]. IEEE Transactions on Autonmatic Control,1999, 44(5):922-937.
[105]MURRAY R, RATHINAM M, SLUIS W. Differential Flatness of Mechanical Control Systems:A Catalog of Prototype Systems[C]//Proceedings of the ASME Interna-tional Congress and Exposition. San Francisco, CA:[出版者不详],1995.
[106]MARTIN P, MURRAY R, ROUCHON P. Flat systems[C]//Proceedings of the 4th European Control Conference.[出版地不详]：[出版者不详],1997：211-264.
[107]NIEUWSTADT M, MURRAY R. Approximate Trajectory Generation for Differen-tially Flat Systems with Zero Dynamics[C]//Proceedings of the IEEE Conference on Decision and Control.[出版地不详]：[出版者不详],1995：4224-4230.
[108]QU Z, WANG J, PLAISTED C. A New Analytical Solution to Mobile Robot Tra-jectory Generation in the Presence of Moving Obstacles[J]. IEEE Transactions on Robotics,2004,20(6):978-993.
[109]ANTRITTER F. On the Relations Between Different Flatness Based Design Methods for Tracking Controllers[C]//Proceedings of the IEEE Conference on Decision and Control. Munchen:[出版者不详],2008：1740-1745.
[110]解学书.最优控制理论与应用[M].北京：清华大学出版社,1986.
[111]LEWIS F, SYRMOS V. Optimal Control[M]. USA:John Wiley & Sons, Inc.,1995.
[112]PACEJKA H B, BAKKER E. The Magic Formula Tyre Model[J]. Vehicle System Dynamics,1991,21(5):1-18.
[113]PACEJKA H B, BAKKER E. Magic Formula Tyre Model with Transient Proper-ties[J]. Vehicle System Dynamics,1997,27(18):234-249.
[114]郭孔辉.汽车操纵动力学[M].吉林：吉林科学技术出版社,1991.
[115]郭孔辉.轮胎侧偏特性的一般理论模型[J].汽车工程,1990,12(3)：1-12.
[116]郭孔辉.各向摩擦系数不同条件下轮胎力学特性的统一理论模型[J].中国机械工程,1996,7(4)：90-93.
[117]Guo K H. A Unified Non-Steady Non-Linear Tyre Model Under Complex Wheel Motion Inputs Including Extreme Operating Cconditions[J]. JSAE Review,2001, 22(4):395-402.
[118]丁海涛.轮胎附着极限下汽车稳定性控制的仿真研究[M].吉林大学：博士论文,2003.
[119]杨得军,林柏忠,管欣.用于开发型驾驶模拟器的17自由度汽车动力学仿真的数学模型及算法[J].汽车技术,2002,12：6-9.
[120]SAYERS M, HAN D. A Generic Multibody Vehicle Model for Simulating Handling and Braking[J]. Vehicle System Dynamics,1996,25:599-613.
[121]RAJAMANI R. Vehicle Dynamics and Control[M]. USA:Springer,2006.
[122]GILLESPIE T. Fundamentals of Vehicle Dynamics[M]. USA:SAE International, 1992.
[123]席裕庚,耿晓军,陈虹.预测控制性能研究的新进展[J].控制理论与应用,2000,17(4)：469-475.
[124]GARCIA C, PRETT D, MORARI M. Model Predictive Control:Theory and Practice —a Survey[J]. Automatica,1989,25:315-348.
[125]陈然.汽车轮胎监测及控制系统的研究[M].重庆大学：硕士论文,2005.
[126]曹兴举.汽车在高速公路上爆胎原因及防治措施[J].交通科技与经济,2008,2：56-57.
[127]纵滔,高进东,程飞.西宝高速公路交通事故分析[J].交通医学,2001,15(6)：593-595.
[128]傅建中,石勇.轮胎气压检测与爆胎自动减速系统[J].汽车工程,2006,28(2)：199-202.
[129]王英麟.基于CarSim与UniTire的爆胎汽车动力学响应研究[M].吉林大学：硕士论文,2006.
[130]黄江,郭孔辉,宋晓琳. 爆胎汽车的稳定性控制[J]. 中国机械工程,2009,20(16)：2006-2010.
[131]RIEKERT P, SCHUNK T. Zur Fahrmechanik des gummibereiften Kraftfahrzeugs[J]. Ingenieur Archiv,1940,11:210-224.