基于车路协同的行人车辆碰撞风险识别与决策方法

为提高行人与车辆碰撞风险识别和预警效果,研究车路协同(CVIS)环境中,多行驶状态下行人与车辆碰撞风险识别与决策过程。基于CVIS平台获取车辆直道行驶和换道行驶状态信息,考虑驾驶人反应状态、车辆运动状态对碰撞风险的影响。引入风险区域预判风险,将当前相对运动状态与决策阈值进行比较,分级识别碰撞风险。采用提醒避碰判别法实时提醒驾驶人当前驾驶状态,使其采取相应的阶梯式双重避碰措施实现避碰决策。仿真结果表明,随着车辆行车速度的增加,进行预警的起始距离和起始时间均呈增加趋势,符合实际情形,该方法考虑了影响人车碰撞的多种因素,能够对碰撞风险进行分级识别和预警。     

手机导航方式对驾驶行为的影响研究

为研究驾驶员驾驶时使用不同手机导航方式对驾驶行为的影响,开展模拟驾驶试验,利用眼动仪获取4个场景下车辆行驶状态和驾驶人视觉参数;通过均值比较,方差和显著性分析,探究不同手机导航方式下驾驶行为存在的差异。结果表明:不同手机导航方式均造成驾驶分心,但分心程度不同;手持手机导航使驾驶人对前方和左侧区域的关注下降最为显著;使用导航时,驾驶人一般通过降低车速来减少分心带来的潜在风险,其中手持手机导航降低幅度最大;使用手机导航时,车辆纵向速度标准差更加集中,说明此时驾驶人对于车辆的控制变强。     

倒计时信号交叉口赶绿灯行为建模与分析

为探究绿灯信号倒计时末尾时间对驾驶入通过交叉口行为的影响规律,在其交叉口进口道不同地点,用雷达测速仪采集不同末尾时间车辆通过的速度数据。基于地点、时间、速度这3类数据,分析驾驶人的赶绿灯行为。采用SPSS软件分类处理数据,并建立赶绿灯行为模型,得到赶绿灯行为的影响因素与规律。结果表明:当绿灯倒计时剩余时间越短而车辆距停车线越远时,车速呈集中分布,并且驾驶人对车速的期望值与当前车速值的比值越大,驾驶人越倾向于采取激进型赶绿灯行为。当绿灯倒计时剩余时间越长而车辆距停车线越近时,车速呈离散型分布,且分布区间大,驾驶人多采取保守型赶绿灯行为。     

认知分心时驾驶人应激反应特征分析

为预防由认知分心影响驾驶人应激反应能力引发的交通事故,开展试验,在驾驶模拟器平台上模拟城市、乡镇、山区和高速公路等应激场景,测试有无分心任务2种条件下驾驶人的应激反应,采集驾驶人操作数据和车辆行驶数据,并利用Facelab5型眼动仪记录驾驶人眼动数据;选取相关表征指标并分析试验数据。结果表明:相比于正常驾驶,认知分心驾驶导致驾驶人视觉搜索范围变窄;无论正常驾驶还是认知分心驾驶,扫视行为均以中、低幅度为主;认知分心导致驾驶人对加速踏板的控制能力减弱;与正常驾驶相比,认知分心时驾驶人的应激反应时间明显增加。     

基于换道行为的驾驶分心识别方法

为预防驾驶分心导致的交通事故,利用径向基函数(RBF)神经网络模型,研究驾驶分心识别方法。通过驾驶模拟试验,分析驾驶人分别在正常驾驶、手持接听电话和免提接听电话等3种状态下执行车辆换道操作时的驾驶行为,构建基于最小正交二乘法(OLS)的RBF神经网络驾驶分心识别模型,用于判定驾驶人是否处于分心状态。研究表明:驾驶分心对换道过程中车辆的纵向速度、横向速度、横向加速度、方向盘转角、方向盘转速和油门开度等6项驾驶绩效参数有显著影响,所构建模型的平均识别正确率达到88. 7%,可准确识别驾驶人的分心状态,为分心事故预防提供理论支撑。     

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形式越复杂,接管过程安全性越差,视觉分心任务与操作分心任务对接管行为影响显著;驾驶人更倾向于选择制动操作接管车辆,次任务分心程度越高,制动接管比例越大;与乘客聊天对接管行为影响不显著,看视频和玩手机游戏均会显著延长接管反应时间,增加工作负荷与车辆纵向减速度,玩手机游戏还会显著提升车辆横向加速度。     

驾驶分心对隧道段行车安全影响分析

为研究驾驶分心对隧道段行车安全的影响,在虚拟驾驶环境下设计次任务试验,要求被试驾驶人执行多组不同类型、难度的手动与无手动次任务,同时利用眼动追踪装置采集驾驶人视觉特征参数。在筛选有效数据的基础上,运用统计学与数据挖掘方法比较驾驶人在隧道内执行不同次任务时,车辆运行速度离散性、视觉搜索区域面积、瞳孔面积变化率和次任务持续时间的差异,并分析统计显著性。结果表明,驾驶人在执行手动次任务、无手动次任务、无次任务3种状态下,执行手动次任务时车辆运行速度离散性最大、视觉搜索区域面积最小、瞳孔面积变化率最大,次任务持续时间最长;无次任务时车辆运行速度离散性最小、视觉搜索区域面积最大、瞳孔面积变化率最小;无手动次任务的试验结果居于两者之间,次任务持续时间最短。上述差异具有统计学显著性,主观感知评价与客观数据具有一致性。研究表明,手动与无手动次任务使驾驶人心理负荷明显增大,手动次任务对驾驶人的影响最为明显,因此驾驶分心次任务对隧道段行车安全影响较大。     

Near-crash事件中驾驶人行为特征分析

为研究不同驾驶人在追尾事故中的驾驶行为特征,用Near-crash事件代替真实碰撞事件,选取一段城市快速道路开展实车试验。首先测试21名驾驶人实驾时的最大减速度、制动至最大减速度时间、平均减速度、碰撞时间倒数(TTCi)4个指标;然后用Mobileye等设备提取数据,得到不同性别、驾驶经验、驾驶风格的驾驶人指标因素;最后对数据进行方差分析。结果表明:Near-crash事件中,女性驾驶人平均减速度、最大减速度大于男性驾驶人,女性驾驶人更倾向于急刹车;经验影响驾驶人的平均减速度、最大减速度;熟练驾驶人制动到最大减速度时间长,制动过程更加平稳;激进型驾驶风格的驾驶人车头时距(THW)小于保守型驾驶人。     

基于减速度的车辆主动避撞模型研究

为预防碰撞类事故,提高驾驶安全性,构建一种以避撞减速度为评价指标的主动避撞模型(DAC)。模型以车辆避撞时所需的最小减速度为系统阈值,将驾驶者的制动延迟时间定为1.2 s;针对不同危险场景下的临界避撞条件,提出以α为目标参数的避撞报警算法;并通过仿真,初步分析DCA模型与时间模型(TTC);然后利用模拟驾驶仪等设备进行DCA模型和TTC模型的对比试验。结果表明:在避撞过程中DCA模型与TTC模型都具有良好的预警能力,但在避撞率和及时性方面,DCA模型分别比TTC模型提高了5.4%和1.02 s。     

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。     

Driver Behavior During Overtaking Maneuvers from the 100-Car Naturalistic Driving Study

Objective: Lane changes with the intention to overtake the vehicle in front are especially challenging scenarios for forward collision warning (FCW) designs. These overtaking maneuvers can occur at high relative vehicle speeds and often involve no brake and/or turn signal application. Therefore, overtaking presents the potential of erroneously triggering the FCW. A better understanding of driver behavior during lane change events can improve designs of this human–machine interface and increase driver acceptance of FCW. The objective of this study was to aid FCW design by characterizing driver behavior during lane change events using naturalistic driving study data.

Methods: The analysis was based on data from the 100-Car Naturalistic Driving Study, collected by the Virginia Tech Transportation Institute. The 100-Car study contains approximately 1.2 million vehicle miles of driving and 43,000 h of data collected from 108 primary drivers. In order to identify overtaking maneuvers from a large sample of driving data, an algorithm to automatically identify overtaking events was developed. The lead vehicle and minimum time to collision (TTC) at the start of lane change events was identified using radar processing techniques developed in a previous study. The lane change identification algorithm was validated against video analysis, which manually identified 1,425 lane change events from approximately 126 full trips.

Results: Forty-five drivers with valid time series data were selected from the 100-Car study. From the sample of drivers, our algorithm identified 326,238 lane change events. A total of 90,639 lane change events were found to involve a closing lead vehicle. Lane change events were evenly distributed between left side and right side lane changes. The characterization of lane change frequency and minimum TTC was divided into 10 mph speed bins for vehicle travel speeds between 10 and 90 mph. For all lane change events with a closing lead vehicle, the results showed that drivers change lanes most frequently in the 40–50 mph speed range. Minimum TTC was found to increase with travel speed. The variability in minimum TTC between drivers also increased with travel speed.

Conclusions: This study developed and validated an algorithm to detect lane change events in the 100-Car Naturalistic Driving Study and characterized lane change events in the database. The characterization of driver behavior in lane change events showed that driver lane change frequency and minimum TTC vary with travel speed. The characterization of overtaking maneuvers from this study will aid in improving the overall effectiveness of FCW systems by providing active safety system designers with further understanding of driver action in overtaking maneuvers, thereby increasing system warning accuracy, reducing erroneous warnings, and improving driver acceptance.     

考虑驾驶员反应时间的车辆碰撞预警模型

为了保证车辆在行驶过程中的安全性,提出了一种考虑驾驶员反应时间的车辆碰撞预警模型,改进了传统模型中驾驶员反应时间定值化的缺点。首先,依据车辆的制动过程分析了驾驶员反应时间对制动距离的影响。其次,设计驾驶员反应时间的模糊推理算法,选取驾龄、疲劳强度和应变能力3个主要因素作为评价指标来计算反应时间。最后,采用分等级的预警策略建立考虑驾驶员反应时间的碰撞预警模型,并通过Carsim-Matlab/Simulink联合仿真与传统模型进行对比分析。结果表明,设计的预警模型可以对不同类型的驾驶员进行差异化碰撞预警,在30 km/h和80 km/h两种车速下实际停车距离与理论值的最大误差为8%。     

Modeling drivers’ reaction when being tailgated: A Random Forests Method

Background: Tailgating is a common aggressive driving behavior that has been identified as one of the leading causes of rear-end crashes. Previous studies have explored the behavior of tailgating drivers and have reported effective solutions to decrease the amount or prevalence of tailgating. This paper tries to fill the research gap by focusing on understanding highway tailgating scenarios and examining the leading vehicles’ reaction using existing naturalistic driving data. Method: A total of 1,255 tailgating events were identified by using the one-second time headway threshold criterion. Four types of reactions from the leading vehicles were identified, including changing lanes, slowing down, speeding up, and making no response. A Random Forests algorithm was employed in this study to predict the leading vehicle’s reaction based on corresponding factors including driver, vehicle, and environmental variables. Results: The analysis of the tailgating scenarios and associated factors showed that male drivers were more frequently involved in tailgating events than female drivers and that tailgating was more prevalent under sunny weather and in daytime conditions. Changing lanes was the most prevalent reaction from the leading vehicle during tailgating, which accounted for more than half of the total events. The results of Random Forests showed that mean time headway, duration of tailgating, and minimum time headway were three main factors, which had the greatest impact on the leading vehicle drivers’ reaction. It was found that in 95% of the events, leading vehicles would change lanes when being tailgated for two minutes or longer. Practical Applications: Results of this study can help to better understand the behavior and decision making of drivers. This understanding can be used in designing countermeasures or assistance systems to reduce tailgating behavior and related negative safety consequences.     

Effects of lead time of verbal collision warning messages on driving behavior in connected vehicle settings

IntroductionUnder the connected vehicle environment, vehicles will be able to exchange traffic information with roadway infrastructure and other vehicles. With such information, collision warning systems (CWSs) will be able to warn drivers with potentially hazardous situations within or out of sight and reduce collision accidents. The lead time of warning messages is a crucial factor in determining the effectiveness of CWSs in the prevention of traffic accidents. Accordingly, it is necessary to understand the effects of lead time on driving behaviors and explore the optimal lead time in various collision scenarios.MethodsThe present driving simulator experiment studied the effects of controlled lead time at 16 levels (predetermined time headway from the subject vehicle to the collision location when the warning message broadcasted to a driver) on driving behaviors in various collision scenarios.ResultsMaximum effectiveness of warning messages was achieved when the controlled lead time was within the range of 5 s to 8 s. Specifically, the controlled lead time ranging from 4 s to 8 s led to the optimal safety benefit; and the controlled lead time ranging from 5 s to 8 s led to more gradual braking and shorter reaction time. Furthermore, a trapezoidal distribution of warning effectiveness was found by building a statistic model using curve estimation considering lead time, lifetime driving experience, and driving speed.ConclusionsThe results indicated that the controlled lead time significantly affected driver performance.Practical applicationsThe findings have implications for the design of collision warning systems.     

汽车EPS系统的行车工况补偿控制策略

针对汽车在常见转向工况时转向的操纵性和安全性不足问题,提出将常见工况补偿控制策略融合到汽车EPS助力控制中,将汽车速度、汽车质量、方向盘左右晃动考虑在内,设计了高速避让、低速满载、颠簸路面3种补偿控制策略;并理论分析加入高速避让、低速满载、颠簸路面3种补偿策略后,方向盘、齿条位移和汽车横摆角速度增减值的变化;最后,通过搭建汽车EPS系统模型和二自由度汽车模型进行仿真验证。仿真结果表明,采用补偿控制策略后,汽车在高速、低速大重量及于凹凸不平的路面行驶时,助力电机目标电流的获取与汽车行驶的内外环境紧密相连,且可随行车条件的改变而适时改变,使汽车EPS系统具有了更好的操纵性和安全性。     

Characteristics of vehicle-animal crashes in which vehicle occupants are killed

During the past 10 years almost 1,500 people have been killed in motor vehicle collisions with animals. Police reports on 147 fatal vehicle-animal crashes during 2000-2002 were obtained from nine states. The goal was to determine common crash types, types of animals involved, and steps that could be taken to reduce the crashes and injuries. Seventy-seven percent of the struck animals were deer, but six other types of animals were involved including small ones such as dogs. Eighty percent of the crashes were single-vehicle events. In most of these cases a motorcycle struck an animal and the rider came off the vehicle, or a passenger vehicle struck an animal and then ran off the road; in a few cases the animal went through the windshield. Multiple-vehicle crashes included vehicles striking deer that went through the windshields of oncoming vehicles, vehicles striking animals and then colliding with other vehicles, and vehicles striking animals that subsequently were struck by other vehicles. Crashes occurred primarily in rural areas, on roads with 55 mph or higher speed limits, during evening or nighttime hours, and in darkness. Greater application of deer-vehicle collision countermeasures known to be effective is needed, but it is noteworthy that a majority of fatalities occurred from subsequent collisions with other vehicles or objects, not from animal contacts. Sixty-five percent of motorcyclists killed were not wearing helmets, and 60% of vehicle occupants killed were unbelted; many of these fatalities would not have occurred with proper protection.     

三岔型枢纽互通事故高发匝道的形成机制与安全提升研究*

为研究高速公路三岔型互通右转匝道车辆事故的发生机制,以宜宾至叙永高速公路双桥枢纽互通为对象,运用Carsim/trucksim软件建立事故匝道的三维数字模型,模拟小客车和货柜车的运行过程,设置3种不同工况,对车辆在匝道上的动力学特性进行分析。结果表明:行驶速度升高会导致匝道路段的车辆横向偏离增大,发生侧滑或侧翻的几率增加;充分制动距离是保证车辆安全通过匝道受限路段的重要因素,货车需要更长的制动距离;道路视觉环境是影响驾驶人速度选择行为的重要因素,匝道路段与高速公路主线行驶环境的高度近似,导致驾驶人选择较高的速度进入匝道,部分车辆在小半径弯道之前无法将速度降低至安全速度,进而发生事故。本文运用行车动力学仿真和驾驶人视觉手段,在驾驶行为层面分析事故的形成机制,进而提出安全提升措施,可为匝道线形设计和交通运行管理提供科学依据。     

Analysis of effects of driver’s evasive action time on rear-end collision risk using a driving simulator

Introduction: Driver’s evasive action is closely associated with collision risk in a critical traffic event. To quantify collision risk, surrogate safety measures (SSMs) have been estimated using vehicle trajectories. However, vehicle trajectories cannot clearly capture presence and time of driver’s evasive action. Thus, this study determines the driver’s evasive action based on his/her use of accelerator and brake pedals, and analyzes the effects of the driver’s evasive action time (i.e., duration of evasive action) on rear-end collision risk. Method: Fifty drivers’ car-following behavior on a freeway was observed using a driving simulator. An SSM called “Deceleration Rate to Avoid Crash (DRAC)” and the evasive action time were determined for each driver using the data from the driving simulator. Each driver tested two traffic scenarios – Cars and Trucks scenarios where conflicting vehicles were cars and trucks, respectively. The factors related to DRAC were identified and their effects on DRAC were analyzed using the Generalized Linear Models and random effects models. Results: DRAC decreased with the evasive action time and DRAC was closely related to drivers’ gender and driving experience at the road sections where evasive action to avoid collision was required. DRAC was also significantly different between Cars and Trucks scenarios. The effect of the evasive action time on DRAC varied among different drivers, particularly in the Trucks scenario. Conclusions: Longer evasive action time can significantly reduce crash risk. Driver characteristics are more closely related to effective evasive action in complex driving conditions. Practical Applications: Based on the findings of this study, driver warning information can be developed to alert drivers to take specific evasive action that reduces collision risk in a critical traffic event. The information is likely to reduce the variability of the driver’s evasive action and the speed variations among different drivers.     

Automatic imitation of risky behavior: a study of simulated driving in china

Objective: Imitation of risky behaviors among drivers is a potentially dangerous threat to driving safety but is infrequently discussed in the existing literature. To enrich the understanding of drivers' imitation behaviors on the road, 2 experiments were designed for a simulated traffic environment. Methods: Safe and risky behaviors were demonstrated by model vehicles separately in the 2 experiments, and imitation behaviors of the participants were observed and analyzed. Results: From experiment 1 it was found that the following distance of participants (measured in time headway) was affected by the distance demonstrated by other vehicles on the road. The influence was stronger when the speed was low, and the participants imitated both risky and safe behavior models. When the speed was high, the participants tended to only learn safe behaviors. In experiment 2, when approaching yellow lights, it was examined whether a driver's decision (pass or stop) would be affected by the behavior of another vehicle (the model vehicle), which was designed to either pass through or stop at the intersection. When the model vehicle ran the yellow light, 65 percent of the participants did the same, even though they were 30?m behind the model vehicle. In contrast, if the model vehicle stopped at the intersection, only 25 percent of the participants decided to pass. Conclusions: It was found that both novice and experienced participants had the tendency to imitate what they saw but were rarely aware of the influence by other drivers in both scenarios.