汽车易驾驶性评价的随机驾驶员模型方法
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摘要
当前，随着汽车的普及及其所面临人群的多样化及高龄化，汽车不能仅仅满足高水平专业驾驶员易于驾驶，还要关注广普人群的驾驶特点，使具有更多不同特性的普通驾驶员可以轻松舒适操纵汽车。本文对汽车易驾驶性的内涵定义为：汽车的设计除应使优秀驾驶员操纵时,系统的理想跟随响应尽可能好之外,还需要充分考虑驾驶员的生理限制和操纵特点以及人的不确定性因素，尽可能保证系统在非优秀的普通驾驶员操纵时仍具有良好的跟踪响应。
汽车设计都要以减轻驾驶员使用负担、提高使用舒适性和安全性等为出发点。汽车操纵性能的分析与设计一直都是各国学者和工程师们的主要研究方向和难题之一。对汽车操纵特性的分析有一个明显特点：汽车和驾驶员的响应互动最为密切。操纵性能的分析不仅要涉及汽车对驾驶员输入响应的准确性、迅速性，驾驶员还会根据感知到的汽车响应再产生控制（响应）输入。简言之，人车之间的关系是以人为主导的相互作用、相互配合、相互制约的关系。
由于汽车易驾驶性的研究对象包括驾驶员群体和汽车，驾驶员的操纵特性具有多样化、随机化的特点。对于确定性的汽车，控制输入在一定范围内具有随机性与不确定性。在实际分析中，有两种主要方法可用来分析汽车适应人群的能力：⑴通过大量真实驾驶员在模拟器或原型样车上进行试验获得；⑵通过数字化驾驶员模型在计算机上进行虚拟仿真分析及预测。进行实车或模拟器的实验要求有原型车或驾驶模拟器以及大样本真实驾驶员群体,试验设备昂贵，人员、时间耗资巨大。而仿真试验可预先通过试验研究建立驾驶员群体操纵特性数据库，针对确定性车辆模型在计算机上实现仿真分析，开发成本小。
在预开发阶段，整车性能的对标是帮助产品设计定位及分析竞争车辆性能的重要途径之一。对标分析同市场需求一起将期望性能转化为技术指标，这些技术指标是后续汽车开发中产品性能按照既定期望实现的保障。对标车辆没有详细车辆参数，通过实车场地试验获得全面车辆性能指标，试验消耗巨大。通过建立对标车辆模型在计算机上分析可有效提高开发效率和成本，将增强计算机技术在设计阶段的应用力度。但是“正向”机理建模方式需要获取大量车辆参数，需要各种测量设备、试验测量纷繁复杂；而通过“逆向”系统辨识技术的建模方法从特定实验的输入输出数据分析中即可获得车辆模型的等价数学模型形式。系统辨识技术不要求汽车详细设计参数，这种方法因其低成本、建模快而具有广阔的工程潜力。
综上所述，本文试图探索一种汽车易驾驶性的闭环分析方法，期望能在计算机上完全通过数字仿真的方法，代替大量真实驾驶员试验，进行汽车操纵性能适应人群能力的分析；并且为进一步扩大计算机仿真分析技术在汽车研发中的应用，提出了一种在对标阶段通过系统辨识技术建立简单车辆模型的快速、简捷、低成本的方法。
本文在广泛调研的基础上，分别对以下问题展开研究：
第一，快速、简捷、低成本建立车辆模型。研究二自由度车辆模型传递函数特点，将速度因素作为自变量离析出原始传递函数，提出了分离速度因素的等效二自由度车辆模型传递函数表达式。对方向盘转角角脉冲输入试验数据进行单输入单输出的车辆模型辨识，结合分离速度因素的等效二自由度车辆模型传递函数表达式外推了任意速度车辆模型，并且通过和方向盘角阶跃输入场地实车试验验证对比模型建立的正确性。辨识数据采用速度为100km/h的方向盘角脉冲实验，通过与不同速度和不同稳态侧向加速度下的方向盘角阶跃输入实车场地试验数据对比，验证所辨识模型的正确性。这种方法简单易操作，满足预开发阶段在没有详细车辆参数规格情况下，获得主要的车辆动力学特性的要求，并且可以大大降低实车试验工作量，使计算机仿真预测能力扩展至预开发阶段从而有效提高开发效率和成本。
第二，为了准确描述驾驶员行为，需要分析驾驶员行为特性，驾驶员模型中必要参数的确定是模型完整性的关键环节。描述驾驶员操纵汽车的行为特性参数很难通过物理仪器量测，通过系统辨识方法可以从驾驶过程中的数据信息提取建模需要的参数。通过广泛调研分析方向控制驾驶员模型的特点后，考虑方向与位置的最优预瞄加速度模型（PO驾驶员模型）运算简单、物理意义明晰，并且对于拟直线预期路径和大角度转向运动的仿真都有良好的跟随精度，本文选择PO模型作为待辨识驾驶员模型。考虑实验过程中的闭环驾驶安全性问题，很难采用广谱随机信号激励作为输入进行辨识获得驾驶员模型的频响特性。采用时域方法识别其ARMA模型参数可以有效解决只能以正常驾驶的短时信号为输入激励的驾驶员模型频响特性的辨识问题。PO驾驶员模型中物理参数的获得还需进一步通过频响特性采样点拟合物理参数。由于频响特性是PO驾驶员模型物理参数的复杂非线性有理分式表达式，对PO驾驶员模型的物理参数的拟和采用阻尼最小二乘法获得，结果显示识别收敛性好且比较稳定。
第三，汽车易驾驶性评价的随机驾驶员模型仿真方法。本文通过对目前国内外针对闭环系统性能评价方面做了广泛调研，并且将其分为三类：1.基于最优闭环性能的评价问题；2.基于对驾驶员生理感知特性或建模参数的分析；3.以驾驶员负担分析作为闭环性能的依据。本文的特点是考虑了驾驶员多样性和随机性，即对于确定性的汽车，驾驶员的控制输入在一定范围内具有随机性与不确定性而导致的闭环系统随机性。因此，首先依据数理统计针对驾驶员群体建立随机驾驶员模型，然后建立人车随机闭环响应指标及其安全性指标以及所对应的随机闭环响应指标及其安全性指标。为了对汽车在随机驾驶员输入下的闭环响应结果进行统计分析，本文采用以概率模型为基础的Monte-Carlo方法，通过抽样随机地模拟驾驶员模型的行为特点同时仿真生成闭环响应，将其随机结果进行分析得到汽车易驾驶性的评价指标。通过比较不足转向汽车、中性转向汽车和过度转向汽车的驾驶员模型参数的分布情况可以初步确定汽车易驾驶性的评价结果。
综合以上，本文有如下创新点：
通过研究2DOFs车辆模型传递函数的特点，本文首次提出分离速度因素的等效二自由度车辆模型传递函数表达式，实现快速简单辨识全速度范围内等价线性2DOFs传递函数车辆模型。在工程应用中可以大大缩减对标阶段的汽车场地操纵稳定性试验工作量。
采用Monte Carlo模拟抽样方法,根据随机驾驶员的人车闭环系统随机响应性能指标探索了汽车易驾驶性性的仿真分析方法，即驾驶员安全驾驶汽车的操纵参数与实际驾驶员群体自身固有操纵参数的匹配能力。
Nowadays, with the vast development of automobile industry, more and more citizens areable to afford family cars, leading to diversification of the driver population. Therefore,vehicle designers should not only satisfy the requirement of professional drivers tomanipulate, but also give adequate consideration to those who are not well-trained,especially the elder ones. It is the engineers`duty to design such vehicle that can be handledby the majority without effort. Ease of control in this paper is the ability of vehicle to adaptto the crowd. In another word, it is the measurement of the size of the population who can, indaily driving behavior, easily control the designed vehicle, under the condition of certainclosed-loop performance to some extent.
Vehicle design should take these points into consideration: relieving the driving burden,enhancing the ride comfort and improving the safety performance. The challenge ofanalyzing and designing vehicle handling performance is always the major research directionof worldwide professors and engineers. The obvious feature for analyzing the vehiclehandling performance is the necessary involvement of drivers; cause vehicle and driverinteract deeply. Not only the quickness and precision of vehicle response should beconsidered during handling performance analysis, but also the response properties of driversto the vehicle state. In short, vehicle and driver as in the close loop could not be separated,because they interact deeply under the domain of human.
Currently, there are mainly three methods of analyzing vehicle handling performance,⑴subjective evaluation which carried out by skilled drivers during verification and tune phase, ⑵objective evaluation in proving ground with test instrument vehicles,⑶objectiveevaluation with simulation methods during definition design and verification. However,subjective evaluation is the final sign. The vital disadvantage of subjective evaluation is lackof coherence. Objective evaluation has its advantages: explicit experiments standard andindicators and without human being uncertainties. Furthermore, with profound developmentof virtual simulation technology and accurate construction of vehicle model, analysis andpredicting the vehicle dynamics based on computer simulation are much easier to realize,and prediction of vehicle handling performance could be made before prototype vehicle orduring benchmarking stage, leading to optimization design with cost and time saving.
The research objects of vehicle ease of control include the driver crowd and vehicles. Asvehicle handling characteristics have the features of diversification and randomization, for aspecific vehicle, the input of control is of randomness and uncertainty within a certain range.For this character, in the actual analysis of vehicle ease of control there are two mainmethods. One method is by well-trained drivers through lots of experiments in simulator orin prototype vehicle, which requires expensive test hardware, such as prototype vehicle anddriving simulator, and consumes insupportable labor and time costs. The other one is bydigital driver model through virtual simulation analysis and prediction in computer, whichcan be very efficient and cost saving as the simulation experiment can be realized bycomputer software, according to a specific vehicle model from the data base of handlingperformance of the driver crowd. The data base is constructed through experimental researchin advance.
Therefore, the aim of this thesis is mainly to explore a closed-loop approach to analysisvehicle ease of control, expecting to carry on the analysis completely through digitalsimulation, instead of lots of real experiments by fully-trained drivers. And this thesispresents a quick, uncomplicated and cost saving method of constructing simple vehiclemodel through system identification technology in benchmarking phase, which could furtherexpand the use of computer simulation technology in automobile research and development. Based on extensive reviews, the following questions were studied:
First, because there exist nonlinear element, complex co-couple between the outputs existswithin multiple-input multiple-output vehicle system, it is not easy to use systemidentification technique to set up vehicle model. It is necessary to make some suitableassumption for the nonlinear problem. Furthermore, considering vehicle speed as input couldnot be a persistent excitation and the relative simple vehicle model could meet the need inbenchmarking phase, a simple but fast vehicle modeling procedure via system identificationtechnique was proposed and the transfer function of the2DOFs vehicle model was identified.First, the single input single output of the vehicle model was identified as the steering angleis the input. According to the transfer function property, vehicle speed was separated asindependent variable in the yaw rate to front steering angle and Latac to front steering angletransfer function. To achieve the data, steering wheel angle pulse test at the speed of100Km/h was carried out, and the vehicle model via the given identification proposal was setup and comparison with the test data from steering wheel angle step test at different speedand lateral acceleration. The coherence between them leads to the validity of the method.This method is simple and easy to carry out and meet the pre-development stage in casethere is no detailed specification of vehicle parameters, and since it could derive the majorvehicle dynamics properties for analyzing, it is an efficient and cost-effective method to setup vehicle model.
Fourier transform method and the ARMA model were used to identify the single input singleoutput vehicle system respectively. When noise exists in the proving ground test data,Fourier method result in low accuracy, because of the inevitable noise in the field test,steering wheel angle is not zero, the presence of residual zero drift, etc. Truncation andleakage problems inherent with Fourier transform when processing data make frequencyresponse characteristics of the estimation accuracy is low. Whereas the ARMA modelmethod could derive a smoother curve of frequency response and without the problem ofinsufficient frequency resolution. Due to its simple and consistency, ARMA model method is a promising tool to derive the frequency response in engineering practice. In addition,since it is a kind of parameter identification method, the results could be used to get theidentified2DOFs vehicle model with speed as an independent variable.
Second, in order to accurately describe the driver's behavior, identification of driver modelparameters through experiment should be carried on. Only when the car was controlledunder drivers, the identification could be done. Because drivers’ perceptual function as thefeedback loop could not be cut off during driving, it is closed-loop system identificationproblem. In accordance with the closed-loop system identification theory, to use indirectclosed loop identification approach, the driver model should be set up first, and thentransform the closed loop to open one to identify. Based on extensive study on the drivermodel theory, considering the Position and Orientation optimal preview driver model (POdriver model) has the advantage of simple and clear physical meaning, and accuracy forfollowing great curvature path, the PO model was chosen as the identified driver model. Theidentified parameters were classified into2categories: structure identification and parameteridentification. The structure parameters Td and Tp were used for identify the otherparameters during parameter identification process; whereas the parameters derived fromparameter identification process provide optimization direction for the structureidentification algorithm. The difference between the sample points of frequency responsefrom ARMA method and that from the identified parameters driver model was minimized byLevenberg-Marquardt algorithm to fit the physical parameters of the PO driver model.
Third, ease of control evaluation via stochastic driver model through simulation wasexplored. In this paper, extensive research over the worldwide range for closed-loop systemperformance analysis was reviewed, and was divided into three categories:⑴based on theevaluation of the optimal closed-loop performance;⑵based on the perceptualcharacteristics of the driver physiological or modeling parameters;⑶analysis of driver'sburden as indication of closed-loop performance. Obvious feature of this thesis isconsidering the driver diversity and randomness; the driver's control inputs with randomness and uncertainty cause the closed-loop system randomness as well. Therefore, first of allstochastic driver model was built based on statistics, and then closed-loop performanceindicators and safety metrics were set up. Using Monte-Carlo method, statistical result of theclosed-loop response under random driver input was analyzed. By comparing theundersteeing car, neutral steering car and oversteer car, the random closed loop performancewere compared, and indicate that the method does work well for the ease of controlevaluation.
Above all, this thesis has the following innovation points:
1. Proposed quick and easy vehicle model identification method through studying thecharacteristics of the2transfer functions derived from2DOFs vehicle model.
2. Using Monte Carlo method, ease of control evaluation was explored by sampling therandom driver parameters.
汽车设计都要以减轻驾驶员使用负担、提高使用舒适性和安全性等为出发点。汽车操纵性能的分析与设计一直都是各国学者和工程师们的主要研究方向和难题之一。对汽车操纵特性的分析有一个明显特点：汽车和驾驶员的响应互动最为密切。操纵性能的分析不仅要涉及汽车对驾驶员输入响应的准确性、迅速性，驾驶员还会根据感知到的汽车响应再产生控制（响应）输入。简言之，人车之间的关系是以人为主导的相互作用、相互配合、相互制约的关系。
由于汽车易驾驶性的研究对象包括驾驶员群体和汽车，驾驶员的操纵特性具有多样化、随机化的特点。对于确定性的汽车，控制输入在一定范围内具有随机性与不确定性。在实际分析中，有两种主要方法可用来分析汽车适应人群的能力：⑴通过大量真实驾驶员在模拟器或原型样车上进行试验获得；⑵通过数字化驾驶员模型在计算机上进行虚拟仿真分析及预测。进行实车或模拟器的实验要求有原型车或驾驶模拟器以及大样本真实驾驶员群体,试验设备昂贵，人员、时间耗资巨大。而仿真试验可预先通过试验研究建立驾驶员群体操纵特性数据库，针对确定性车辆模型在计算机上实现仿真分析，开发成本小。
在预开发阶段，整车性能的对标是帮助产品设计定位及分析竞争车辆性能的重要途径之一。对标分析同市场需求一起将期望性能转化为技术指标，这些技术指标是后续汽车开发中产品性能按照既定期望实现的保障。对标车辆没有详细车辆参数，通过实车场地试验获得全面车辆性能指标，试验消耗巨大。通过建立对标车辆模型在计算机上分析可有效提高开发效率和成本，将增强计算机技术在设计阶段的应用力度。但是“正向”机理建模方式需要获取大量车辆参数，需要各种测量设备、试验测量纷繁复杂；而通过“逆向”系统辨识技术的建模方法从特定实验的输入输出数据分析中即可获得车辆模型的等价数学模型形式。系统辨识技术不要求汽车详细设计参数，这种方法因其低成本、建模快而具有广阔的工程潜力。
综上所述，本文试图探索一种汽车易驾驶性的闭环分析方法，期望能在计算机上完全通过数字仿真的方法，代替大量真实驾驶员试验，进行汽车操纵性能适应人群能力的分析；并且为进一步扩大计算机仿真分析技术在汽车研发中的应用，提出了一种在对标阶段通过系统辨识技术建立简单车辆模型的快速、简捷、低成本的方法。
本文在广泛调研的基础上，分别对以下问题展开研究：
第一，快速、简捷、低成本建立车辆模型。研究二自由度车辆模型传递函数特点，将速度因素作为自变量离析出原始传递函数，提出了分离速度因素的等效二自由度车辆模型传递函数表达式。对方向盘转角角脉冲输入试验数据进行单输入单输出的车辆模型辨识，结合分离速度因素的等效二自由度车辆模型传递函数表达式外推了任意速度车辆模型，并且通过和方向盘角阶跃输入场地实车试验验证对比模型建立的正确性。辨识数据采用速度为100km/h的方向盘角脉冲实验，通过与不同速度和不同稳态侧向加速度下的方向盘角阶跃输入实车场地试验数据对比，验证所辨识模型的正确性。这种方法简单易操作，满足预开发阶段在没有详细车辆参数规格情况下，获得主要的车辆动力学特性的要求，并且可以大大降低实车试验工作量，使计算机仿真预测能力扩展至预开发阶段从而有效提高开发效率和成本。
第二，为了准确描述驾驶员行为，需要分析驾驶员行为特性，驾驶员模型中必要参数的确定是模型完整性的关键环节。描述驾驶员操纵汽车的行为特性参数很难通过物理仪器量测，通过系统辨识方法可以从驾驶过程中的数据信息提取建模需要的参数。通过广泛调研分析方向控制驾驶员模型的特点后，考虑方向与位置的最优预瞄加速度模型（PO驾驶员模型）运算简单、物理意义明晰，并且对于拟直线预期路径和大角度转向运动的仿真都有良好的跟随精度，本文选择PO模型作为待辨识驾驶员模型。考虑实验过程中的闭环驾驶安全性问题，很难采用广谱随机信号激励作为输入进行辨识获得驾驶员模型的频响特性。采用时域方法识别其ARMA模型参数可以有效解决只能以正常驾驶的短时信号为输入激励的驾驶员模型频响特性的辨识问题。PO驾驶员模型中物理参数的获得还需进一步通过频响特性采样点拟合物理参数。由于频响特性是PO驾驶员模型物理参数的复杂非线性有理分式表达式，对PO驾驶员模型的物理参数的拟和采用阻尼最小二乘法获得，结果显示识别收敛性好且比较稳定。
第三，汽车易驾驶性评价的随机驾驶员模型仿真方法。本文通过对目前国内外针对闭环系统性能评价方面做了广泛调研，并且将其分为三类：1.基于最优闭环性能的评价问题；2.基于对驾驶员生理感知特性或建模参数的分析；3.以驾驶员负担分析作为闭环性能的依据。本文的特点是考虑了驾驶员多样性和随机性，即对于确定性的汽车，驾驶员的控制输入在一定范围内具有随机性与不确定性而导致的闭环系统随机性。因此，首先依据数理统计针对驾驶员群体建立随机驾驶员模型，然后建立人车随机闭环响应指标及其安全性指标以及所对应的随机闭环响应指标及其安全性指标。为了对汽车在随机驾驶员输入下的闭环响应结果进行统计分析，本文采用以概率模型为基础的Monte-Carlo方法，通过抽样随机地模拟驾驶员模型的行为特点同时仿真生成闭环响应，将其随机结果进行分析得到汽车易驾驶性的评价指标。通过比较不足转向汽车、中性转向汽车和过度转向汽车的驾驶员模型参数的分布情况可以初步确定汽车易驾驶性的评价结果。
综合以上，本文有如下创新点：
通过研究2DOFs车辆模型传递函数的特点，本文首次提出分离速度因素的等效二自由度车辆模型传递函数表达式，实现快速简单辨识全速度范围内等价线性2DOFs传递函数车辆模型。在工程应用中可以大大缩减对标阶段的汽车场地操纵稳定性试验工作量。
采用Monte Carlo模拟抽样方法,根据随机驾驶员的人车闭环系统随机响应性能指标探索了汽车易驾驶性性的仿真分析方法，即驾驶员安全驾驶汽车的操纵参数与实际驾驶员群体自身固有操纵参数的匹配能力。
Nowadays, with the vast development of automobile industry, more and more citizens areable to afford family cars, leading to diversification of the driver population. Therefore,vehicle designers should not only satisfy the requirement of professional drivers tomanipulate, but also give adequate consideration to those who are not well-trained,especially the elder ones. It is the engineers`duty to design such vehicle that can be handledby the majority without effort. Ease of control in this paper is the ability of vehicle to adaptto the crowd. In another word, it is the measurement of the size of the population who can, indaily driving behavior, easily control the designed vehicle, under the condition of certainclosed-loop performance to some extent.
Vehicle design should take these points into consideration: relieving the driving burden,enhancing the ride comfort and improving the safety performance. The challenge ofanalyzing and designing vehicle handling performance is always the major research directionof worldwide professors and engineers. The obvious feature for analyzing the vehiclehandling performance is the necessary involvement of drivers; cause vehicle and driverinteract deeply. Not only the quickness and precision of vehicle response should beconsidered during handling performance analysis, but also the response properties of driversto the vehicle state. In short, vehicle and driver as in the close loop could not be separated,because they interact deeply under the domain of human.
Currently, there are mainly three methods of analyzing vehicle handling performance,⑴subjective evaluation which carried out by skilled drivers during verification and tune phase, ⑵objective evaluation in proving ground with test instrument vehicles,⑶objectiveevaluation with simulation methods during definition design and verification. However,subjective evaluation is the final sign. The vital disadvantage of subjective evaluation is lackof coherence. Objective evaluation has its advantages: explicit experiments standard andindicators and without human being uncertainties. Furthermore, with profound developmentof virtual simulation technology and accurate construction of vehicle model, analysis andpredicting the vehicle dynamics based on computer simulation are much easier to realize,and prediction of vehicle handling performance could be made before prototype vehicle orduring benchmarking stage, leading to optimization design with cost and time saving.
The research objects of vehicle ease of control include the driver crowd and vehicles. Asvehicle handling characteristics have the features of diversification and randomization, for aspecific vehicle, the input of control is of randomness and uncertainty within a certain range.For this character, in the actual analysis of vehicle ease of control there are two mainmethods. One method is by well-trained drivers through lots of experiments in simulator orin prototype vehicle, which requires expensive test hardware, such as prototype vehicle anddriving simulator, and consumes insupportable labor and time costs. The other one is bydigital driver model through virtual simulation analysis and prediction in computer, whichcan be very efficient and cost saving as the simulation experiment can be realized bycomputer software, according to a specific vehicle model from the data base of handlingperformance of the driver crowd. The data base is constructed through experimental researchin advance.
Therefore, the aim of this thesis is mainly to explore a closed-loop approach to analysisvehicle ease of control, expecting to carry on the analysis completely through digitalsimulation, instead of lots of real experiments by fully-trained drivers. And this thesispresents a quick, uncomplicated and cost saving method of constructing simple vehiclemodel through system identification technology in benchmarking phase, which could furtherexpand the use of computer simulation technology in automobile research and development. Based on extensive reviews, the following questions were studied:
First, because there exist nonlinear element, complex co-couple between the outputs existswithin multiple-input multiple-output vehicle system, it is not easy to use systemidentification technique to set up vehicle model. It is necessary to make some suitableassumption for the nonlinear problem. Furthermore, considering vehicle speed as input couldnot be a persistent excitation and the relative simple vehicle model could meet the need inbenchmarking phase, a simple but fast vehicle modeling procedure via system identificationtechnique was proposed and the transfer function of the2DOFs vehicle model was identified.First, the single input single output of the vehicle model was identified as the steering angleis the input. According to the transfer function property, vehicle speed was separated asindependent variable in the yaw rate to front steering angle and Latac to front steering angletransfer function. To achieve the data, steering wheel angle pulse test at the speed of100Km/h was carried out, and the vehicle model via the given identification proposal was setup and comparison with the test data from steering wheel angle step test at different speedand lateral acceleration. The coherence between them leads to the validity of the method.This method is simple and easy to carry out and meet the pre-development stage in casethere is no detailed specification of vehicle parameters, and since it could derive the majorvehicle dynamics properties for analyzing, it is an efficient and cost-effective method to setup vehicle model.
Fourier transform method and the ARMA model were used to identify the single input singleoutput vehicle system respectively. When noise exists in the proving ground test data,Fourier method result in low accuracy, because of the inevitable noise in the field test,steering wheel angle is not zero, the presence of residual zero drift, etc. Truncation andleakage problems inherent with Fourier transform when processing data make frequencyresponse characteristics of the estimation accuracy is low. Whereas the ARMA modelmethod could derive a smoother curve of frequency response and without the problem ofinsufficient frequency resolution. Due to its simple and consistency, ARMA model method is a promising tool to derive the frequency response in engineering practice. In addition,since it is a kind of parameter identification method, the results could be used to get theidentified2DOFs vehicle model with speed as an independent variable.
Second, in order to accurately describe the driver's behavior, identification of driver modelparameters through experiment should be carried on. Only when the car was controlledunder drivers, the identification could be done. Because drivers’ perceptual function as thefeedback loop could not be cut off during driving, it is closed-loop system identificationproblem. In accordance with the closed-loop system identification theory, to use indirectclosed loop identification approach, the driver model should be set up first, and thentransform the closed loop to open one to identify. Based on extensive study on the drivermodel theory, considering the Position and Orientation optimal preview driver model (POdriver model) has the advantage of simple and clear physical meaning, and accuracy forfollowing great curvature path, the PO model was chosen as the identified driver model. Theidentified parameters were classified into2categories: structure identification and parameteridentification. The structure parameters Td and Tp were used for identify the otherparameters during parameter identification process; whereas the parameters derived fromparameter identification process provide optimization direction for the structureidentification algorithm. The difference between the sample points of frequency responsefrom ARMA method and that from the identified parameters driver model was minimized byLevenberg-Marquardt algorithm to fit the physical parameters of the PO driver model.
Third, ease of control evaluation via stochastic driver model through simulation wasexplored. In this paper, extensive research over the worldwide range for closed-loop systemperformance analysis was reviewed, and was divided into three categories:⑴based on theevaluation of the optimal closed-loop performance;⑵based on the perceptualcharacteristics of the driver physiological or modeling parameters;⑶analysis of driver'sburden as indication of closed-loop performance. Obvious feature of this thesis isconsidering the driver diversity and randomness; the driver's control inputs with randomness and uncertainty cause the closed-loop system randomness as well. Therefore, first of allstochastic driver model was built based on statistics, and then closed-loop performanceindicators and safety metrics were set up. Using Monte-Carlo method, statistical result of theclosed-loop response under random driver input was analyzed. By comparing theundersteeing car, neutral steering car and oversteer car, the random closed loop performancewere compared, and indicate that the method does work well for the ease of controlevaluation.
Above all, this thesis has the following innovation points:
1. Proposed quick and easy vehicle model identification method through studying thecharacteristics of the2transfer functions derived from2DOFs vehicle model.
2. Using Monte Carlo method, ease of control evaluation was explored by sampling therandom driver parameters.
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