人-车-路耦合环境中车辆稳态安全仿真

为揭示驾驶员、道路、车辆综合作用下车辆运行状态的失稳机理,基于多Agent建模与安全仿真技术,构建包含驾驶员、道路、车辆、协调中心、人机接口等5方面的多Agent车辆稳态安全仿真框架;建立车辆多体动力学、道路三维空间、驾驶员预瞄控制与跟随仿真模型,从车辆稳定性和驾驶员操作负荷2方面分析车辆稳态安全性。以小轿车为代表车型,针对某二级公路开展车辆稳态安全仿真试验。仿真结果表明,当汽车以60和70 km/h行驶时,前后轮总体围绕平均轴重波动,稳态运行;以80 km/h行驶时,汽车冲出车道,轮胎最小垂直反力仍然大于0;3种速度下侧向加速度均大于0.3 g(g为重力加速度),且速度越高,侧向加速度越大,表明汽车冲出车道由侧滑引起。     

基于SEM的双车道公路超车行为安全评价

为提高驾驶人在双车道公路上超车的安全性,基于tau理论适应性分析,提出超车过程中驾驶人不仅根据tau线索估计避碰时间,也利用距离、速度、加速度等判断超车是否安全的试验假设。招募12名受试者,应用驾驶模拟系统试验平台,依据试验假设采集12个与超车行为有关的驾驶行为数据,对其进行无量纲化处理,并利用Bootstrap法对数据进行扩增。结合驾驶行为问卷得分,利用AMOS软件,建立超车行为安全评价的结构方程模型。通过对初始假设模型进行多次修正与评价,得到影响超车行为安全的3个驾驶行为参数。结果表明,超车车辆在超车并道后的速度、与前导车之间的避碰时间及与对向车辆之间的避碰时间这3个驾驶行为参数可作为超车行为安全评价的有效指标。     

车载GPS数据驱动的危货运驾驶员安全绩效评价

为提高危化品道路运输驾驶员日常安全绩效考核的科学性与便捷性,首先,从车载全球定位系统(GPS)中挖掘自然驾驶数据,运用改进的基于坐标系自适应变换的行驶轨迹分段拟合方法,提取行驶速度、加速度、平面曲率、道路坡度与坡长等动态驾驶数据;然后,结合车辆侧翻车速预测模型、长大下坡制动鼓温升模型,建立行驶速度稳定性、车辆横向稳定性、长大下坡制动稳定性和驾驶状态稳定性4个层次共7个指标的安全评价体系;最后,采用组合赋权法,构建10分制的危化品道路运输驾驶员安全绩效评价模型。研究结果表明:基于坐标系自适应变换的行驶轨迹分段拟合方法平均减少19.23%的拟合残差,构建的评价模型对危险驾驶行为反应灵敏。     

基于视频轨迹参数的边缘率减速标线驾驶行为效果评价方法

为探究边缘率减速标线对驾驶行为与交通安全的作用效果,提出了基于监控视频提取轨迹参数的实证评价方法.采用背景差法和透视变换方法对监控视频进行车辆目标提取和轨迹处理,进而提取车辆速度、加速度、车头时距、车头间距和车道偏移等行车参数;通过参数统计分析,边缘率减速标线对车速控制、跟车距离保持和车道保持都有不同程度的控制效果,但使纵向舒适性变差;为了综合评价其效果,基于层次分析法建立了边缘率标线的综合控制效果评价模型,从有效性、安全性和舒适性3个维度对减速标线进行综合评价.该方法和模型还可用于评价其他不同设计参数的减速标线.     

基于视频数据的城市隧道交通运行特征与安全研究

为降低城市隧道交通事故率,提高车辆出行安全,以上海市翔殷路跨江隧道为研究对象,利用视频采集技术、车辆跟踪调查采集隧道交通运行数据,分析城市隧道交通运行特征及驾驶员在隧道内驾驶行为特征,并进一步研究其与隧道交通事故的关系。对隧道内车辆的速度、密度分布,变道行为进行分析,发现隧道南线入口处内外断面平均车速的显著差异可以解释该处追尾事故高发,隧道内车辆频繁变换车道导致隧道侧碰事故占较大比例。为改善城市隧道交通安全,在隧道内进行限速和限制车辆变道是必要的。     

驾驶人视觉注意力需求与驾驶行为特性的关系

采用驾驶模拟系统平台,以不同车道类型及不同交通流状态(自由流/稳定流/不稳定流/强制流)为虚拟试验场,应用心理试验设计方法,测试得到21名被试在不同交通流状态下的视觉注意力需求及驾驶行为。对5种车道类型及4种交通流状态下被试驾驶时的视觉注意力需求特性和驾驶行为特性数据进行分析,得到不同车道不同交通流状态下的视觉注意力需求变化情况。利用多元回归方法分析了不同交通流状态下驾驶人视觉注意力需求与驾驶行为之间的关系,并构建了基于驾驶行为特性的驾驶人视觉注意力需求预测模型。结果表明,驾驶人视觉注意力需求与制动次数、油门踏板位移和车辆轨迹偏差之间存在相关关系。     

基于驾驶模拟技术的公路隧道车道宽度运行影响分析

对于设计车速为80 km/h的双管6车道高速公路盾构隧道,受盾构机及资金限制,内侧车道设计宽度为3.5 m,相比一般高速公路路段规定减小了0.25 m。以上海市沿江特大高速公路隧道为研究对象,分析车道宽度的这种变化对车辆运行的影响。依照隧道设计方案,分别建立内侧车道宽度为3.75 m和3.5 m的2种模拟场景,基于同济大学高仿真驾驶模拟器展开对比试验。通过试验采集同一驾驶员在2种场景下的车速、轨迹偏移等参数,绘制隧道路段平均车速、平均轨迹偏移分布图,比较2种场景下运行参数平均值差异,发现2种场景下的车速、轨迹偏移变化趋势基本一致。利用配对t检验方法验证参数差异的显著性,结果显示,内侧车道宽度由3.75 m减小为3.5 m,对于车辆速度、轨迹偏移没有显著影响。     

高速公路换道行为的频谱结构识别及分类

为提高高速公路出口换道车辆与主流车辆之间安全性，以驾驶行为功率谱分析为基础，进行换道识别及危险特征分类。首先，采集高速公路出口车辆车头时距、速度、加速度特征数据，从车辆行驶横向、纵向运行方向计算碰撞时间(Time-To-Collision, TTC),以得到危险冲突与一般冲突发生概率。其次，基于特征参数划分换道过程中安全状态，根据换道车辆运动学规律，对换道行为数据抽样，同时进行频谱结构分析，在定阶为10的频谱下构建行为功率谱模型，得到碰撞安全阈值。此外，通过相关性分析得到一般冲突和严重冲突的特征量，并与实际换道数据进行匹配。最后，利用K-means聚类分析对危险行为进行分类。研究结果显示：K-means聚类分析算法的精度较高，能有效辅助交通管制措施。研究成果可为高速公路换道行为研究和高速公路出口复杂交通变化研究提供理论基础。     

未按规定导向车道行驶行为干预方法研究

为保障信号交叉口的正常交通秩序,充分遏制机动车未按规定导向车道行驶行为,亟需探究该行为的影响因素及干预方法。以北京市内4个信号交叉口处共35 h的1 666条监控视频数据为基础,对未按规定导向车道行驶行为进行定义并将其分为9类,分别对频率较高的5类未按规定导向车道行驶行为构建二元Logit模型,以确定其关键影响因素,并据此提出干预方法。结果表明,排队车辆数、大车比例、时段、车流量、照明条件等因素会不同程度地影响5类未按规定导向车道行驶行为的发生概率,其中排队车辆数及时间因素影响最为显著。在此基础上,从交通工程设施及驾驶人安全意识角度,提出优化交叉口渠化设计及信号配时、采用智能标线、强化监管力度及完善交通管控设施、加强驾驶人安全教育4种未按规定导向车道行驶行为干预方法。     

信号交叉口进口道路段车辆换道行为分析

为提高信号交叉口的交通安全管理水平,研究驾驶员换道行为影响因素,确定各因素影响下的换道风险率,选取可能影响变道行为的潜在变量,构建交叉口换道行为的Cox风险分析模型,并以武汉市3个交叉口调查数据为例进行实例分析。结果表明:10个因素对进口道车辆换道有显著影响,其中,车辆与目标车道前方车辆的车头间距、与目标车道后方车辆的车头间距、所在车道与目标车道车辆密度差和可供变道选择的目标车道数是危险因素;车辆与所在车道前方车辆的车头间距、车辆距停车线距离、行驶速度和所在车道相位的绿信比是保护因素。车辆处于信号灯倒计时的变道风险率高于其他时段;出租车的变道风险率高于其他车辆。     

道路运营安全性评价方法与指标

笔者从道路交通设施系统客观安全性和道路用户主观安全性的角度,应用交通冲突分析技术、运行车速预测方法、行车动力学仿真分析[空间视距模型、人工神经网络(ANN)模型],研究并分析车辆在自由流和非自由流两种状态下运营安全性,提出跟驰风险指数、变换车道风险指数、高速行车风险指数,建立了服务于道路安全运营管理的道路交通设施系统及其运营安全性的分析评价方法和指标。     

基于加速度干扰和AHP的车辆行驶安全性评价模型

关于行驶中的车辆在交通系统各因素作用下的行驶安全性评价,在现有交通系统四大要素的基础上加入车辆行驶状态,构成"人-车-环境-路-车辆行驶状态"5要素。并对各要素评价指标的选定做了细致的筛选,建立了车辆行驶安全性评价指标体系。其中"人-车-环境-路"因素采用打分的形式确定其参数的分值,并利用层次分析法(AHP)计算各参数权重来构建模型的;"车辆行驶状态"因素则是引入加速度干扰的定义,建立基于道路结构的加速度干扰模型。最后,运用层级分析法建立了总的车辆行驶安全评价模型。     

Modeling the probability of freeway lane-changing collision occurrence considering intervehicle interaction

Objective: To better capture the relationships between lane-changing collisions and explanatory variables, a microscopic model is developed for freeway lane-changing collisions based on the interactions between lane-changing vehicles.

Methods: The model applies an intervehicle interaction structure to account for the occurrence mechanism of lane-changing collisions. The occurrence mechanism can be described as the failure of a vehicle driver of an adjacent lane in avoiding the lane-changing vehicle, which disturbs the smooth movement of the adjacent lane vehicle and requires the driver's brake action to avoid an angle collision. This model is examined using data collected from freeways in Washington State during 2010 to 2011 and validated using lane-changing collision data for the SR 520 freeway.

Results: The findings of this study show that generalized truck percentage has a significant decreasing effect on lane-changing collision risk, whereas average spacing and several roadway characteristics have significant increasing effects. The frequency of slight collisions during peak hours is higher than that during off-peak hours. Young female drivers are more likely to be involved in collisions during lane-changing than young male drivers, but the result for senior drivers is opposite, with older male drivers having a higher probability of lane-changing collisions than female drivers in the same age group.

Conclusion: The process of lane-changing collisions is a complicated maneuver. Truck percentage, average spacing, and good roadway characteristics, such as straight and level segment, in the target lane have a significant effect on the occurrence of lane-changing collisions. Age and gender are also 2 important factors contributing to the relationship between lane-changing collisions and explanatory variables.     

基于Multi-class SVM的车辆换道行为识别模型研究

自动安全换道是车辆实现无人驾驶的关键,为精确识别行驶车辆换道状态,保证行车安全,设计了一种基于多分类支持向量机(Multi-class Support Vector Machine,Multiclass SVM)的车辆换道识别模型。从NGSIM数据集中选取美国101公路车辆轨迹数据进行分类处理,并将车辆换道过程划分为车辆跟驰阶段、车辆换道准备阶段和车辆换道执行阶段。采用网格搜索结合粒子群优化算法(Grid Search-PSO)对SVM模型中惩罚参数C和核参数g进行寻优标定,利用多分类支持向量机换道识别模型对样本数据进行训练和测试,模型测试精度达97.68%。研究表明,模型能够很好地识别车辆在换道过程中的行为状态,为车辆换道阶段的研究提供支持。     

辅助驾驶中的换道决策安全研究

换道是驾驶员达到快速通行目标的一种常用手段,但换道会带来很多公路交通事故。为有效避免交通事故,需给驾驶员提供换道安全预警。构建了安全换道决策模型,将换道决策分为换道意图识别和换道条件判断分别建立模型以提高预测精确度。通过神经网络方法SOM(Self-Organization-Map)聚类及BP(Back Propagation)建立换道意图识别模型,基于贝叶斯理论建立最小风险贝叶斯换道条件判别模型。模型开发和测试采用车辆轨迹数据集(NGSIM),提取数据中的换道行为特征参数作为模型的输入,将驾驶员换道决策预测视为输入变量的函数。通过对比最小贝叶斯和最小风险贝叶斯方法发现,由后者构建的换道条件判别模型效果较好,对于不换道行为的预测精度为90.4%,换道行为的预测精度为73.8%。鉴于错误的换道决策可能导致交通事故,而错误的不换道决策只会导致失去一次换道的机会,在换道辅助系统中,不换道决策的精确度要求需高于换道决策的精度。最后,在微观交通仿真系统中加入换道决策模型,其结果验证换道决策安全。最小风险贝叶斯换道条件判别模型的引入,使得换道决策系统能够通过修正风险系数,进一步提高换道判别精度,减少不安全的换道概率。     

基于驾驶模拟的自动驾驶接管行为研究

为探索有条件自动驾驶对非驾驶相关任务的允准边界,基于实车驾驶模拟器,设计自动驾驶接管试验典型场景,招募30名被试者开展驾驶模拟试验;要求驾驶人执行3种分心形式的驾驶次任务,系统发出接管请求提示后,驾驶人接管车辆控制权以避免险情发生,并分析驾驶人接管反应时间、驾驶负荷以及驾驶绩效等相关数据.结果表明:驾驶次任务涉及的分心...     

车辆碰撞特性的数值仿真及试验研究

运用显式动态有限元方法建立了某车辆的有限元模型 ,按照国家标准对整车进行了正面碰撞仿真计算和实车正面碰撞试验。通过仿真计算和实验的对比分析 ,其结果表明 :仿真计算的变形结果和试验吻合较好 ,车身上一些主要部位的加速度曲线在趋势上也基本吻合 ,说明运用有限元法进行车辆的仿真切实可行。分析结果为车辆的安全性设计提供了参考依据。     

Reducing primary and secondary impact loads on the pelvis during side impact

OBJECTIVE: The objective of the study was to determine which vehicle factors are significantly related to pelvic injury in side impact collisions. Identification of relevant parameters could aid in the reduction of these injuries. METHOD: Side impact crashes from the CIREN database were separated into those in which the occupant sustained a pelvic fracture and those in which no pelvic fracture occurred, although all occupants had serious injuries. A multibody MADYMO model was created of a USDOT SINCAP (U.S. Department of Transportation Side Impact New Car Assessment Program) test of a vehicle with a large center console. RESULTS: From a study of 113 side impact crashes in the ciren database, nearside occupants with pelvic fractures (n = 78) had (i) more door intrusion (mean, 37 vs. 32 cm, p = 0.02) than those who had serious injuries, but not pelvic fractures (ii) a greater likelihood that the lower border of the door intruded more than the upper part (40% vs. 18%, p < 0.025); and (iii) a greater likelihood that their vehicle had a center console (47 vs. 17%, p < 0.005). Other parameters such as occupant age, weight, gender, vehicle weight, and struck vehicle speed change were not significantly different. MADYMO modeling showed that with a center console, an initial positive pelvic acceleration occurred at about 30 msec, followed at about 45 msec by a second acceleration peak in the opposite direction. Reducing console stiffness reduced the second acceleration but not the initial peak. Allowing the seat to translate laterally when contacted by the door reduced the initial pelvic acceleration by 50% and eliminated the second acceleration peak. CONCLUSIONS: Redesigning the center console using less stiff materials and allowing some lateral translation of the seat could aid in reducing pelvic injuries in side impact collisions.     

Effects of an integrated collision warning system on teenage driver behavior

IntroductionCrash warning systems have been shown to provide safety benefits, but no studies have examined how teenagers respond. This study sought to find out whether young, inexperienced drivers change behavior in response to warnings.MethodsForty 16–17 year-olds drove an instrumented vehicle equipped with a system that warned for lane departures and potential rear-end and lane change/merge crashes. Participants were randomly assigned to experimental or control groups, and their driving was monitored for 14 weeks during 2011–12. For the experimental group, this included a treatment period, when crash alerts were received by drivers, and baseline and post-treatment periods, when warnings were recorded but not received. The control group never received warnings. Data were analyzed to determine whether warnings were associated with changes in driving behavior.ResultsA total of 15,039 trips were analyzed. Lane drifts accounted for 73% of warnings. Forward collision warning rates doubled for all drivers during the treatment period and continued at an increased rate post-treatment. This was likely a result of the fact that, as time went on, all drivers spent more time following vehicles at close distances. Receiving alerts was associated with effects on following and lane-changing behavior, including more time spent following at close distances (17%), fewer lateral drifts (37%) and fewer unsignaled lane changes (80%). Receiving warnings wasn't associated with an increased likelihood of engaging in secondary tasks.ConclusionsWarning systems may result in improved lane-keeping and turn-signal behaviors by novice drivers, but there is some indication they may result in more close-following behaviors.Practical applicationsThere is some evidence that lane departure warning may improve turn-signal use for young drivers. While there is no evidence of safety benefits from the other types of warnings, there is some evidence of an increase in close-following behavior but no increase in secondary tasks due to the presence of those capabilities.