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毕 业 设 计（论 文）任 务 书1本毕业设计（论文）课题应达到的目的： 作为土木工程专业的必修实践环节，毕业设计主要是对学生所学知识和技能的综合运用和训练过程，是对学生素质培养和工程实践能力培养的全面检验，使学生了解和熟悉科学研究的基本环节和研究方法，学会撰写学术论文和学位论文。（1）熟练掌握高速公路的设计原理与相关设计规范；（2）熟练运用Auto CAD及相关软件来进行路线平面设计绘图；（3）熟悉相关道路的纵断面设计；（4）熟悉相关道路的横断面设计；（5）熟悉相关道路总体设计和相关设计。 2本毕业设计（论文）课题任务的内容和要求（包括原始数据、技术要求、工作要求等）： 本设计主要是进行高速公路的路线设计、整理相关计算表格、应用CAD和相关的软件绘图，使学生能够在知识运用、程序开发、文献检索、论文撰写等各个方面有一定的提高，为今后从事高速公路工程项目的设计及施工奠定良好的基础。（1）该道路建设的可行性研究，调查项目区域的地质，地形，自然条件与该道路交叉的其他道路情况；（2）确定路线选线的要求和设计原则；（3）确定道路的技术等级和设计指标；（4）确定路线的控制点选择；（5）确定路线方案；（6）路线的纵断面设计；（7）路线的横断面设计；（8）说明书。毕 业 设 计（论 文）任 务 书3对本毕业设计（论文）课题成果的要求包括图表、实物等硬件要求： （1）高速公路平面图；（2）高速公路的纵断面设计图；（3）高速公路的横断面设计图；（4）逐桩表、单元表、土石方表；（5）路线设计说明书；（6）直线，曲线及转角表；（7）纵断面要素表；（8）路基相关设计表格。4主要参考文献： 1杨少伟.道路勘测设计M.北京:人民交通出版社，2004年.2方守恩.高速公路M.北京:人民交通出版社，2002年.3JTGB01-2003，公路工程技术标准S.4JTGD20-2006，公路路线设计规范S.5JTGD60-2004，公路桥涵设计通用规范S.6邓学钧.路基路面工程M.北京:人民交通出版社，2005年.7邵旭东.桥梁工程M.北京:人民交通出版社，2007年.8周商吾.交通工程M.上海:同济大学出版社，1987年.9廖正环.公路施工技术与管理M.北京:人民交通出版社，2006年.10JTGD62-2004，公路钢筋混凝土及预应力混凝土桥涵设计规范S.11JTJ018-1997，公路排水设计规范S.12邱棣华.材料力学M.北京:高等教育出版社，2004年.13李廉锟.结构力学M.北京:高等教育出版社，2004年.14顾孝烈，鲍峰，程效军.测量学M.上海:同济大学出版社，1999年.15JTJ013-1995，公路路基设计规范S.16JTJ006-1998，公路环境保护设计规范S.17高大钊，袁聚云.土质学与土力学M.北京:人民交通出版社，2003年.18JTJ014-1997,公路沥青路面设计规范S.19陆春其.公路工程造价M.北京:人民交通出版社，2007年.20JTGB03-2006，公路建设环境影响评价规范S.20JTJ022-1985，公路砖石及混凝土桥涵设计规范S.21JTJ023-1985，公路钢筋混凝土及预应力混凝土桥涵设计规范S.22JTJ017-1996，公路软土地基路堤设计与施工技术规范S.23JTG/TD71-2004，公路隧道交通工程设计规范S.24JTJ074-1994，高速公路交通安全设施设计及施工技术规范S.25GBJ50162-1992，道路工程制图标准S.毕 业 设 计（论 文）任 务 书5本毕业设计（论文）课题工作进度计划：2016-01-232016-01-30 道路设计资料收集；2016-02-122016-03-05 完成道路路线方案初步设计；2016-03-062016-03-13 完成翻译；2016-01-232016-04-07 完成道路平纵横设计、工程量计算等；2016-04-082016-05-08 完成并整理设计说明计算书；2016-05-092016-05-14 评阅、答辩。所在专业审查意见：通过负责人： 2015 年 12 月21 日 译文题目： Safety Assessment of Driver Overtaking Behavior on Two-Lane Highways 原文：Safety Assessment of Driver Overtaking Behavior on Two-Lane Highways ABSTRACTSafety study of driving behaviors to reduce traffic accidents is of great significance. The primary objectives of the study were to investigate the relation between driver overtaking behaviors and traffic safety. Twelve drivers of different proficiency were selected for an experiment on a driving simulation system platform. A simulation scene of overtaking on a two-lane highway was set up. The subjects overtook in the virtual scenarios at different speeds. A total of twelve parameters - including speed, acceleration, time of overtaking process, the changes of distance and other parameters - were extracted during the experiments. These data together with the results of a DBQ (Driving Behavior Questionnaire) were analyzed and evaluated by the multiple linear regression method. The results showed that three motion parameters had a strong correlation with drivers safety. Finally, the study presents a linear model of safety assessment of driver overtaking behavior on two-lane highways. The model may help to identify safe and unsafe drivers and reduce the number of traffic accidents.Keywords: safety direct rural simulator dynamic vehiclesINTRODUCTIONTwo-lane rural roads make up the majority of the road network in many countries. In 2009, Chinas rural mileage reached 3.2 million km, and highway mileage of rural roads accounted for more than three-quarters of the total mileage (Li, 2009). Rural roads are also dominant in traffic fatality statistics. Lame et al. (2007) has estimated that more than 60% of all fatalities in traffic occur on two-lane rural roads.Overtaking maneuvers on rural two-lane highways is a common phenomenon. When drivers have potential to overtake and there is sufficient space to overtake on the road, overtaking demands will be created. In the process of overtaking, the driver determines whether there is sufficient and adequate passing sight distance and time headway of the opposing lane and whether there is an adequate inserting gap of the same lane. Then drivers decide whether the overtaking should be implemented. Since overtaking conditions and drivers behavior varies, the overtaking process is very complex. It is affected by road conditions, alignment, sight distance, vehicle type, speed, and drivers, among other things.Greenshields et al. (1935) was the first to establish the minimum requirements for safe passing under average traffic conditions. Bar-Gera, H. and Shinar, D. (2005) have shown this maneuver is associated with an increase in crash risk, because it involves driving in the lane of the opposing traffic direction. At the same time, Harris (1988) found that most drivers are indeed aware that overtaking is a risky maneuver by self-report ratings. At present, many existing studies (see reviews by Tang, 2007; Geertje, et al., 2007; Wei, et al, 2000; Rong,et al., 2007; Shao, et al., 2007) focus on overtaking modeling. A few studies assessing the safety of driving overtaking behaviors generally examine driver gender, age and other characteristics (David, et al., 1998), or by DBQ questionnaire to survey (Ozkan and Lajunen, 2005). It is difficult to obtain drivers real-time direct performance of overtaking maneuvers due to the danger of the experiment on a real road. To assess driving behaviors in overtaking, we employ a driving simulator. Various studies have shown that driving simulators can provide reliable observations of drivers behaviors (Blana, 1996; Desmond, and Matthews, 1997; Van der Winsum and Brouwer, 1997; Ellingrod et al. 1997.). In this study, we focus on individual differences in the safety of overtaking maneuvers with DBQ and real-time behaviors on a driving simulator. The results of the study are designed to distinguish unsafe drivers from other kinds of drivers.METHODParticipantsTwelve volunteers participated in the experiment. The sample comprised six men and six women between 24 and 55 years old (M= 28.08 years, S.D. = 5.6). There was an equal balance of males and females. All had a valid drivers license. The mean number of years driving experience was 5.25 years and, on average, drivers drove 3.08 hours per week. All had normal or corrected-to-normal visual acuity, and did not take any kind of medicine.ApparatusA full-size advanced driving simulator (KMRTDS, developed by the simulation laboratory of Faculty of Transportation in Kunming University of Science and Technology, China) was used in this study. The simulated vehicle cab, an Axial, featured all normal displays and controls (steering, brakes, and accelerator) found in a vehicle. Different driving scenarios were projected onto a 1500 cycle screen, with sound effects of the vehicles in motion broadcasted by two-channel amplifiers. With the optimized image processing speed of more than 30 frames per second and the calibration for the speed and visual, KMRTDS can ensure the real-time of the system and the fidelity of the experimental scene. Outlook of the driving simulator is shown in Figure 1. KMRTDS can provide up to 68 motion parameters to analyze the behaviors of the vehicle and drivers.Experimental designThe purpose of this experiment is to evaluate the safety of driving behaviors during driver overtaking at different speeds on two-lane highways. In China, the largest design speed of a two-lane highway is 80km/h. On the other hand, the sight distance of overtaking is different with the different speeds of experimental vehicles. In this experiment, we set the speed of experimental passed and oncoming vehicles to 30, 37.5, 45 and 60km/h respectively. The corresponding overtaking speeds were set at 40, 50, 60 and 80km/h. These four groups of overtaking processes were randomly distributed in four sections of equal length on a two-lane highway. All subjects will drive on these four sections in the scenarios, and there were 48 observations in the experimentDriving scenariosThe parameters of overtaking driving scenarios include static and dynamic parameters. The static parameters refer to the road alignments, traffic signs, markings and the surrounding natural environment and so on. The dynamic parameters indicate the parameters of dynamic driving vehicles, including the triggering movement regions of experimental vehicles in the scenarios, speed, driving route, distance between vehicles and so onStatic scenariosThe design of the static overtaking scenario of this experiment (shown in Figure 2) is an approximately square scenario, composed of four straight sections of a rural secondary two-lane highway. Total distance is 8.0 km, with 2.0 km long on each side, and the turning radius of the connection is 200 meters. The width of a single lane is 4.5 m (including the road shoulder), with no isolation facilities in the center. Both sides of the road are grass and randomly distributed trees and villages, and each section is mounted with the speed limit and directional signs. The visual field of the scenario is open in that there are no obstacles on either side. Meanwhile, the field has good climate and normal traffic conditions, that is to say,no fog, no rain, no snow weather and dry flat pavement conditions. The static scenario design of experiment is shown in Figure 1Dynamic scenarios helpIn accordance with highway standards in China, specific section with overtaking sight distance in appropriate distance should be set on a two-lane highway based on need and terrain. Pie and Wang (2004) have stated the length of the section should not less than 10%-30% of the total length of the route in normal circumstances.In dynamic scenarios, the experimental vehicles must drive according to pre-determined routes and speeds. When the leading vehicle which the driver maneuvered reached a specific place, the other experimental vehicles will be triggered to start movement. These places are triggering points. The setting of dynamic parameters of experimental vehicles during overtaking process was explained as follows.Figure 1. Design of static scenarioFigure2 is the experimental dynamic scenario after loaded traffic flow. Figure 2. Dynamic overtaking scenarios in KMRTSMeasurementsOvertaking Behavior MeasurementThe subjects were required to drive in the KMRTS in order to determine the driver subjects overtaking behavior. Every subject finished four tests in the scenarios. There are 48 different overtaking behavior measurements. When they drove, they should increase their speed as close to the speed limit according to limit speed signs in the simulated scenarios (including 40,50,60,0KM/H, a total of four speeds), and then complete an overtaking maneuver in each section according to their personal experience and ability. But once they completed an overtaking maneuver and on turning sections, there was no limitation on speed.During the experiment, the drivers operation behavior, driving speed, acceleration, overtaking time, distances traveled, distance between the overtake car and oncoming cars before and after overtaking were recorded. All these data were used to evaluate the drivers driving behavior.Self-report measurementA DBQ was introduced as an self-report questionnaire survey, which was adapted by Sullman and others based on Reasons Driving Behavior Questionnaire (2002). The questionnaire is divided into three dimensions, namely, violations of traffic rules, error behaviors and offensive violations, containing twenty items. In this questionnaire, we used the Likert five-point scoring method to record the drivers scores. The scores are described by centesimal grades. After calculating the mean values of all driver subjects total score, we can conclude the average intent of a group on different dimensions. In addition, we can understand each individuals attitude scores distribution situation by this progressive mean scale.Determination of beginning and ending time of overtaking processesTiming the beginning and ending of the overtaking process includes: the time when the driver begins to overtake, begins to return to the previous lane and completes the overtaking. Determination of these moments enables us to get the travel time and distance traveled at all stages in the process of overtakingThe determination of the moment when drivers begin to overtake can be divided into two situations: (1) The time a driver turns on a left turning signal before overtaking and (2) if the driver did not turn on a left turning signal, the time determined by mathematical methods. This fixes the driving trajectory by two straight lines. Time to overtake corresponds to the intersection of two lines from the moment the driver begins to overtake.The end time of the overtaking process was the moment the vehicle returns to the original lane. We used a video playback with stopwatch to record the moments when drivers completed the overtaking maneuver completely and returned to the original lane. The moment was defined as the time a driver completed overtaking. Based on the start and end time of the drivers whole overtaking process, we can obtain the vehicle speed, acceleration, steering angle and other motion parameters in the period of overtaking in the following analysis of experimental results.RESULTS AND DISCUSSIONOvertaking performanceThe results of twelve drivers operation behaviors and performances in four speeds during the overtaking are shown in Table 2.Table 1. Drivers Overtaking Behavior under Four SpeedsSpeedlimiOvertaking performanceTurn signal performanceSuccess OvertakenProportion of Subject No. of noproportionCar. No.turn signalturn signal40km/h100%Car283.3%No.8,No.1250km/h100%Car2, Car375%No.8,No.9,No.1260km/h100%Car283.3%No.8,No.1280km/h66.7%Car2,Car3,Car44 75%No.8,No.11,No.12Table 2. Statistics of Overtaking BehaviorSubjectNo.Speed limit of 80KM/HAverage values of four speedsTp(s)Vavg(km/h)Sc(m)Tp(s)Vavg(km/h)Sc(m)17.6989.543.4612.6354.7533.0326.1395.8985.549.6479.8463.07311.0985.001.7112.6767.3926.1641.3989.90-16.749.4260.3526.69515.5689.7018.2511.3677.9150.0769.2494.40-2.3211.0479.2538.1378.1982.0030.0010.4392.8853.47811.3589.70184.6210.6292.16132.4397.4780.80196.3210.2273.2389.221013.6487.5022.1413.8554.0742.95110.0091.400.007.4674.9380.57129.1088.7121.6610.4562.0854.59On different speed limit roads, driver subjects all completed the overtaking process successfully except for 80km/h. Among them, on the speed limit 40km/h and 60km/h sections all overtook Car2, but on the speed limit 50km/h section, driver subjects No.7, 8, and 12 followed the front car for too long a time and overtook too late, so they only overtook Car3. In the speed limit 80km/h section, due to the higher speed, driver subjects No.1, 6, 7, and 11 did not complete the overtaking process, and driver subjects No. 8 and9 had overtaken Car4 and Car3. When drivers turned on a turning signal during the overtaking process, driver subjects No.8 and 12 did not have the habit of turn on a turning signal, which would affect overtaking process safety.To further understand the drivers safety trends at different speeds, three parameters were selected for further analysis. That is, Tp (the completing time for the entire overtaking process), Sc (the distance between the main car and oncoming cars when returned to the original lane after completed the overtaking process) and Vavg (average overtaking speed). Table 3 shows the overtaking behavior parameters results of driver subjects at speed limit 80KM/H and the average speed of four.In Table 3, the negative values (-16.74 and -2.32) means there was a collision with oncoming cars before completing overtaking, while the value 0.00 indicated not overtaking any oncoming car in the whole overtaking process, namely the overtaking process was not completed. Compared to other speed limits, it was relatively difficult to complete the overtaking process on the speed limit 80km/h section. The average time drivers completed the overtaking process was 8.4s, shorter than the average time of the three other speeds. Except for driver 11, the average speed of other drivers in the overtaking process was 88km/h, a high speed in the two-lane secondary highway, because the speed of the front car and oncoming cars was 60km/h. If the driver slightly hesitated or slowed down, he would miss the best opportunity for overtaking. Even if the spacing between the oncoming cars was large enough, the driver would not risk an overtaking, especially with greater chances of colliding with the oncoming car.In terms of the average Top, Vague, Sc of the twelve driver subjects under four speed limit conditions, we found that the cases of smaller Tp and Sc and larger Vavg reflected the condition of the drivers overtaking strategy. The strategy is that drivers will improve their own safety by reducing the conflict opportunity with other vehicles when they estimate the overtaking conditions. But at this point, the drivers may be in an unsafe overtaking. For example, we can conclude the driver subject No.4 is a safer driver rather than No.3.Self-report on usual driving behaviorsThe DBQ questionnaire results for twelve subjects were analyzed with the use of a Linker five-point scoring method. Each item has five selections which grades from 1 to 5 points. The 20 items score 100. The higher the score, the safer the drivers driving behavior. The scores of DBQ (S_DBQ) are between 77 and 95 points. The subject No.3 got the highest score, while subject No.4 the lowestFurther analysis from the scores of three dimensions shows that subjects had good judgments about error behaviors (S_EJ: M=29.5, D=5) and for offensive violations (S_AV: M=26.08, D= 6). Comparing the two dimensions, there are more violations of traffic rules among individual drivers (S_OR: M=28.42, D=9 S_OR). The results are shown in Figure 4It can be stated that twelve subjects identified aggressive violations and misjudgments as unsafe driving behaviors, but some overlooked traffic rule violations. On the other hand, with the results of subjects overtaking performance and DBQ questionnaire, we found that although the DBQ reflected their subjective driving experience, there are still great differences in real-world driving.Safety assessment MODEL on overtakingTo evaluate the safety of subjects in the overtaking process, a multiple linear regression analysis was used to judge their behaviors combined with driving behavior questionnaire score and their motion parameters in the overtaking process.We selected DBQ questionnaire score “y” as the dependent variable for the regression equation, and identified initially twelve motion parameters which probably influenced theirsafety as independent variables, with x1, x2, . x12 indicated in Table 4 .Selection of influencing variablesTable 3. Description of Twelve Motion Parameters during OvertakingVariablesDescriptionVariablesDescriptionx1Acceleration whenstarting to overtakeX7Distance traveled during overtakingX2Speed when startingto overtakeX8Average speed during overtakingX3Acceleration whenreturning to laneX9Distance between the main vehicle and the vehicle in the opposite lane when overtaking isfinished (WOF)X4Speed whenreturning to lanex10Distance between the main vehicle and the passed vehicle WOF.X5Time spent duringovertakingx11Distance between the main vehicle and the vehicle in the opposite lane when overtaking begins (WOB).X6Averageacceleration duringovertakingx12Distance between the main vehicle and the passed vehicle WOBWe analyzed the correlation among twelve influencing independent variables in 45 of 48 observations ( three observations were redundant). The correlation coefficient between variables is Table 4. Correlation Coefficient MatrixVarx1X2X3X4X5X6X7X8X9x10x11x12yx11.00X20.011.00X30.060.251.00X40.070.510241.00X5-0.32-0.15-0.080.291.00X60.32-0.46-0.030.230.091.00X7-0.220.290.060.620.78-0.071.00X80.030.590.060.890.110.000.521.00X90.14-0.16-0.550.02-0.220.38-0.290.101.00x100.29-0.100.050.220.240.110.270.17-0.071.00x11-0.210.37-0.060.340.50-0.100.730.28-0.16-0.131.00x12-0.060.05-0.330.110.450.090.490.000.09-0.080.551.00y-0.240.060.380.050.09-0.130.11-0.01-0.450.19-0.11-0.211.00shown in Table 5. From the or relation coefficient matrix in Table 5, there is a strong correlation between some variables (the data with *)Modeling of multiple linear regressionTable 5. Results of Multiple Linear Regression AnalysisObservationsMultipleRR2Adjusted R2St.errorFSig450.6780.4600.3925.026.847.66E-4FactorsEst.indexSt. errort StaticSig.ToleranceVIFConstant73.9284.87515.1660.000x80.1370.0642.1440.038.9911.009x9-0.0460.015-3.0870.004.9701.031x100.1100.0482.2820.028.9791.021Because there were strong correlations between some variables, a multivariate regression analysis using a stepwise method was performed After the excluded variables, the optimized model including variables x8, x9 and x10 are the influencing factors, as shown in Table 6From Table 6, we can get predictors in the model, which are constant, x8, x9 and x10. There is no multi-nonlinearity among the predictors because of VIF5. The model is not so good in that the chi-square goodness of fit test is weak. It indicated that there were some other nonlinear correlations between them. The model of multi-linear regression obtained is as follows：Y = 73.928 + 0.137 * X 8 + 0.046 * X 9 + 0.11* X 10 （3）These three selected variables showed that the safety of overtaking behavior is attributed to the speed and some distances during overtaking. The faster the speed is, the safer the behavior is。CONCLUSIONSafety assessment of driving overtaking requires at least two things: first is a method providing the drivers overtaking maneuvers and second a model assessing and controlling for the safety of overtaking behaviors.The proposed drivers overtaking safety assessment model assumes that average speed and some distances are relevant to the safety of overtaking. The distances include the distances from the passing vehicle to the passed and oncoming vehicle when the overtaking ended. The model relies on a stepwise method of multiple linear regression analysis, using twelve parameters in a simulation experiment as independent variables and DBQ scores as the dependent variable.In our results, the model was certainly somewhat limited by two factors. The first was the selection and quantity of subjects, and the second was the difference between driving simulators and on-road experiments. Concerning the first factor, we must be aware that DBQ has subjective results. Moreover, there were 48 observation values, this experiment had only twelve subjects. We introduced twelve influencing factors under inadequate data; there was greater influence among variables. All these will influence the validity of the model and produce some errors.Dealing with the comparability of the driving simulator and on-road experiments, previous studies have made sure there are parallel observations. Based on these studies, we also can conclude that with the advent of more powerful graphics processors and renderers, simulators are increasingly appealing for studying and training drivers.This work was a first approach to the problem. In future work, we plan to improve the safety assessment prediction model by increasing the number of subjects and by using other methods to build the model中文译文：双车道公路上驾驶员超车行为的安全性评估摘要安全驾驶行为的研究对减少交通意外具有伟大的意义。这项研究的主要目的是探讨司机超车行为和交通安全的关系。十二个不同能力的司机被选定为驾驶模拟系统实验平台。成立一个双车道的高速公路上超车的模拟场景。在虚拟场景以不同的速度进行超越。在这个实验提取了十二个参数，包括速度，加速度，超车过程中的时间， 距离和其他参数的变化。这些数据连同一个DBQ（违例驾驶行为问卷）的结果，进行了分析和评价的多元线性回归方法。结果显示司机的安全与三个运动参数有一个密切的关系。最后,研究提出了一种线性模型的司机超车行为在双车道高速公路的安全评估。该模型可以帮助识别司机的安全和不安全意识来减少交通事故的数量。关键词：安全 直接的 农村的 模拟器 动态的 车辆引言在许多国家双车道的农村公路占据了绝大多数的公路网络。在2009年，中国农村里程达到3.2万公里，高速公路在农村公路里程占到总里程超过四分之三。农村道路交通死亡人数的统计也占主导地位。拉姆等人（2007年）估计，超过60的交通死亡发生在两车道的乡村公路上。农村双车道公路上的超车是一种常见的现象。当司机有潜在的超越意识和有足够的超越空间,超车的情况将会发生。在超车的过程中，司机决定是否有足够和充分的超车视距和车头时距，以及是否有一个足够的插入同一车道的差距。然后司机决定是否超车。由于超车条件和司机行为会有所不同，超车的过程是非常复杂的。它受道路条件、视觉距离、车辆类型,速度和司机,其他事情的影响。Greenshields et al。（1935年）是第一个建立的最低平均流量条件下安全通过的要求。Bar-Gera, H. 和Shinar, D. (2005)显示这种操作是增加引起交通事故风险，因为它涉及到驾驶在内线的相反的方向的交通。同时，哈瑞斯（1988）发现，大多数司机确实知道超车是危险动作的自评报告。目前,许多现有的研究(参见评审,2007;Geertje唐等人,2007;魏、等,2000;荣)等人,2007;Shao等人,2007)关注超车建模。一些研究评估安全驾驶的超车行为一般检查司机的性别、年龄和其他特征(大卫,等人,1998),或者通过DBQ问卷方式调查(主刀和拉尤宁就,2005)。很难获得司机超车时的直接动作由于危险的实验在一个真正的道路。我们雇佣驾驶模拟器来评估驾驶行为的超车。不同的研究都表明,驾驶模拟器可以提供可靠的观察驾驶员的行为(林松柏;1996年,德斯蒙德,和马修斯,1997;Van der Winsum和Brouwer,1997;Ellingrod et al . 1997年)。在本研究中,我们关注个体差异的安全演习DBQ超车和实时行为在驾驶模拟器。这项研究的结果被设计用来区分不安全的司机和其他类型的司机。方法参与者十二个志愿者参加了这个实验。示例包含6男6女24岁和55岁之间(M = 28.08年,s.d = 5.6)。有一个平等的男性和女性。都有一个有效的驾照。平均驾驶经验5.25年,平均每周3.08个小时开车。一切有正常或矫正到正常视力，并没有采取任何一种药。 仪器一个全尺寸的先进的驾驶模拟器(中国昆明理工大学KMRTDS所开发的仿真实验室的教员)是在这项研究中的应用。模拟车辆驾驶室, 一辆夏利, 普通显示器的特色和控件(转向、制动器、和加速器)中发现的车辆。不同的驾驶场景被投射到1500年周期的屏幕,车辆的音效在运动中由双通道播放器放大。与优化图像处理的30帧/秒的速度和校验和视觉KMRTDS确保了系统的实时和实验场景的逼真度。驾驶模拟器的应用前景是图1所示。KMRTDS可以提供多达68运动参数,分析了车辆和司机的行为。实验设计这个实验的目的是评估司机超车在不同速度下双车道高速公路上安全的驾驶行为。在中国,最大的双车道公路的设计速度是80公里/小时。另一方面,视线是超车的距离与不同速度不同实验车。在这个实验中,我们设定的实验速度分别是30,37.5,45至60公里/小时迎面而来的车辆到。这个相应的超车的速度被设定在40、50、60和80公里/小时。这四组的超车过程被随机分布在四个部分长度相等的两车道的公路上。有48名实验观察者，所有的受试者将行驶于这四个部分的场景。驾驶场景超车的参数驱动的场景包括静态和动态参数。静态参数指的是道路的路线、交通标志、标记和周围的自然环境。动态参数表示参数的动态驾驶车辆,包括引发运动区域的实验车在场景中,速度、行驶路线、车辆等之间的距离。静态场景静态超车的设计实验的场景是连续四个部分组成的一个农村中学两车道的公路的场景。总距离是8.0公里,两边长2.0公里,连接的转弯半径是200米。一条单行道的宽度为4.5米(包括路肩),没有隔离设施的中心。道路两边的是草和随机分布的树木和村庄,每一节都安装了限速,指示标志。视野的场景是开放的,不存在任何障碍两边。与此同时,该领域具有良好的气候和正常的交通状况,也就是说,没有雾,没有雨,没有雪的天气和干燥的平坦路面条件。静态场景实验设计如图1所示。在按照高速公路标准，在中国的特定部分根据需要和地形在适当的距离超车视距应设置双车道公路。裴和小王(2004)已经声明部分的长度在正常情况下不小于总长度的10%-30%。在动态情况下,实验车必须根据预先确定的路线和开车速度。当主要汽车司机操纵达到特定的地方,另一个实验的车辆将被触发开始运动。图1 静态场景的设计测量超车行为测量为了确定司机受试者的超车行为受试者被要求开车去KMRTS。每一个主题完成了四个测试 场景。测量48个不同的超车行为。他们应该增加他们的速度，接近模拟场景限速（包括40,50,60,80公里/小时，共限制速度标志4个速度），然后在每节完成超车他们个人的经验和能力。但是一旦他们完成了超越机动部分,没有速度限制。在实验过程中,司机的操作行为,行车速度、加速度、超越时间、距离,距离超过汽车和迎面而来的汽车取代之前和之后都被记录下来。所有这些数据被用来评估司机的驾驶行为。自我报告的测量介绍了一种DBQ作为自我报告的问卷调查,其结果Sullman调整和其它基于理性的驾驶行为问卷调查(2002)。问卷分为三个层面,即,违反交通规则,错误的行为和进攻违规,包含20个项目。在此问卷,我们使用李克五点打分的方法来记录司机的分数。分数是成绩的描述。通过所有受试司机的总得分计算均值, 我们可以得出结论：一组不同尺寸的平均意图。此外, 我们可以通过这种渐进的平均得分的分布情况规模理解每一个人的态度。开始和结束时间的确定的超车过程司机的开头和结尾超车过程包括:当司机开始超车时，开始返回到前面的车道，完成超车。这些时刻的决心，使我们能够得到旅行时间和距离行驶在超车过程中的各个阶段。司机开始超车时的那一刻的决心分为两种情况：（1）司机打开一只剩下转信号在超车的时间（2）如果司机没有打开左转向信号，超车前数学方法确定的时间。这修正驾驶两条直线的轨迹。间超过对应的十字路口,此刻两行司机开始超越。超车过程的时刻结束,车辆的回报原始的车道。我们使用一个视频回放有秒表记录的时刻当司机超车完成和放回原车道。目前被定义为一个司机超车的时间完成。基于的开始和结束时间驱动程序的整体超车过程,我们可以获得车辆速度、加速度,方向盘角度和其他运动参数时期的超车以下实验结果的分析。结果和讨论超车性能在不同的车速限制的道路上，驾驶员科目全部成功完成超车处理除了为80km的/小时。其中,在限速40公里/小时和60公里/小时部分取代汽车2所有,但在限速50公里/小时,司机在科目7、8、12遵循前面的车时间太长和取代太晚了,所以他们只追上汽车3。在限速80公里/小时一节，由于更高的速度，驾驶员科目1，6，7，11，未完成。8 and9，4和3取代了。当司机打开转向灯了超车过程中,司机和12号没有打开转向灯,这将影响到超车过程安全。十二个司机的操作行为和表现在四个超车速度如表1所示。表1 司机超车行为在4个速度限制速度超车性能转向信号性能成功超越没有标题号的比例比例汽车号转向信号转向信号40km/h100%Car283.3%8号 12号50km/h100%Car2, Car375%8号 9 号 12号60km/h100%Car283.3%8号 12号80km/h66.7%Car2,Car3,Car44 75%8号 11号 12号为了进一步