使用车载信息装置对驾驶行为的影响研究

为研究使用车载信息装置对驾驶行为的影响,基于虚拟驾驶仿真平台,开展正常驾驶、操作按键式和触摸屏式收音机驾驶分心试验;应用外周视觉检测任务法(PDT)测量驾驶人反应时间、PDT目标命中率以及车速和跟驰距离等参数,以评估分心行为的心理资源需求及其对驾驶绩效的影响。试验结果表明:使用车载信息装置会降低驾驶人反应能力和驾驶绩效,操作触摸屏分心与操作按键分心相比,驾驶人反应时间延长了167 ms,PDT目标命中率降低了10%,速度保持和车距保持能力下降,使用触摸屏式车载信息装置需要更多的心理资源,对两侧视野内的信息刺激反应明显迟钝,对行车安全有较大影响。     

冰雪弯道多频视觉参照系对车速感知的影响

为保障冰雪环境下弯道路面的行车安全,定量分析不同速度条件下,不同弯道多频率组合标志对驾驶员车速感知的影响。采用3dmax软件建立冰雪弯道仿真模型,基于E-prime2. 0软件进行车速感知心理物理试验,探究在不同组合频率、不同速度条件下,驾驶员的速度感知能力和差异。研究结果表明:高中低频组合的视觉参照系能使驾驶员产生一定高估速度的错觉,高频12 Hz、中频1 Hz、低频0. 3 Hz的组合频率标志诱导速度高估的效果最佳;随着速度的增大,速度高估逐渐减小,速度感知差异增大;合理设置多频率组合标志,有利于保障冰雪弯道行车安全。     

驾驶人弯道安全行车特性研究

为研究在弯道路段行驶过程中驾驶人的安全驾驶特性,招募30名驾驶人开展模拟驾驶试验。利用DLab驾驶人因记录分析系统采集弯道行驶过程中驾驶人的车辆操作数据,用face LAB 5非接触式眼动仪同步采集驾驶人的眼动数据,探讨弯道半径对驾驶人视觉及操作模式的影响。结果表明:水平方向上驾驶人的视线主要集中在[-10°,10°],水平视角均值与弯道半径成二次函数关系,并随着弯道半径增大驾驶人的水平视线向右偏移;在操作模式方面,转向盘转角与弯道半径成负相关,车辆横向位置与弯道半径的关系不明显。驾驶人弯道行车时需要及时关注弯道一侧的交通信息,并同时操纵车辆沿弯道轨迹行驶。     

注意偏侧化对驾驶视觉搜索的眼动表现证据

为探究驾驶过程中伪忽视注意在不同道路等级条件下视觉搜索偏好的差异,通过实车驾驶的试验方法,记录15名驾驶人分别在3种真实道路环境(高速公路、快速公路、二级公路)驾驶过程的眼动数据(兴趣区、注视时长、注视点、瞳孔大小),以便发现驾驶人空间注意的视觉眼动搜索模式。结果表明：驾驶人首先表现出轻微偏左的不对称空间注意的特点,随着道路条件的复杂程度提高(从高速公路、快速公路到二级公路),驾驶人认知负荷提高,驾驶空间注意的眼动搜索呈现出趋中到趋右的安全搜索模式倾向。     

驾驶人行为不确定性对车辆轨迹预测的影响分析

针对驾驶行为不确定性影响车辆轨迹预测精度的问题，采用预瞄控制方法和滑模控制方法，分析了不同行驶车速与工况下车辆轨迹预测误差。结果表明：预瞄控制方法和滑模控制方法能够有效处理驾驶人超车、避障和转向行为的外部干扰，将轨迹预测误差控制在0.5 m以内，但高速工况下的轨迹误差明显大于低速工况下的轨迹误差；在小曲率转向工况下，基于单点预瞄驾驶人视觉轨迹预测误差小于基于双点预瞄驾驶人视觉轨迹预测误；在大曲率转向工况下，基于单点预瞄驾驶人视觉轨迹预测误差大于基于双点预瞄驾驶人视觉轨迹预测误。研究结果为驾驶人视觉轨迹预测策略的制定及车辆不同行驶工况下驾驶人行为不确定性的处理提供了理论依据。     

前车突然切入时驾驶人应激生理特性研究

为研究前车突然切入对驾驶人生理负荷的影响,利用MP150生理监测系统对22名被试进行虚拟驾驶试验。采集记录前车突然切入时被试的生理参数。研究驾驶人心率增长率和心率变异性(HRV)指标与车速、应激距离之间的关系。结果表明:自车速度为100 km/h时,随着前车切入距离从55.6 m减小到27.8 m,被试的平均心率增长率从16.21%增大到23.27%,HRV参数低频(LF)值也呈现下降趋势。前车切入距离一定,随着自车车速从60 km/h增加到120 km/h,被试的平均心率增长率存在显著性差异,平均从13.05%上升到21.85%。差异性检验结果表明,前车切入距离和自车速度发生变化时驾驶人的生理负荷变化趋势一致,但自车速度因素对驾驶人生理负荷的影响程度高于切入距离因素。     

基于期望安全车距的车辆跟驰行为分析与仿真

为解释实际道路交通流中的跟车距离差异,引入期望及参照点理论,提出期望安全车距(ESD)概念,分析其主要影响因素。采用C#编程模拟车辆跟驰行为,以跟车距离的试验值作为ESD的代表值,从横向角度考察ESD的个体差异,分析性别及实际驾龄对ESD的影响,并采用单因素方差分析(ANOVA)方法对比分析不同驾驶类型间的差异。数据显示:驾驶人ESD的个体差异较大,性别对ESD有一定影响,实际驾龄对ESD的影响无明显规律。ANOVA分析表明:驾驶类型对ESD有极大影响(P0.001)。ESD是驾驶人判断车距是否安全的参考点,是导致实际跟车距离存在差异的主要原因。     

视觉与听觉次任务对驾驶人视觉的影响及差异

为研究视觉与听觉次任务对驾驶人视觉的影响及差异性,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、难度的视觉与听觉次任务,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执行不同次任务时,视觉搜索区域面积、瞳孔面积、次任务完成时间的差异并分析统计显著性。结果表明,驾驶人在执行视觉次任务、听觉次任务、无次任务3种状态下,执行视觉次任务时视觉搜索区域面积最小、瞳孔面积变化幅度最大、次任务完成时间最长;无次任务时视觉搜索区域面积最大、瞳孔面积变化幅度最小、执行次任务时间最短;执行听觉次任务时,视觉特征数据居于视觉次任务、无次任务两种状态之间。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,视觉与听觉次任务使驾驶人心理负荷明显增大,且视觉次任务对驾驶人的影响更为明显。     

隧道内多频多尺寸信息对车速感知的影响

为提高隧道内的行车安全性,定量分析隧道内不同尺度、不同频率的视觉信息对驾驶员车速感知的影响。利用3DMax软件制作公路隧道内仿真模型。利用E-prime软件进行车速感知心理物理试验,分别研究大中小尺度信息在频率为0.1~32 Hz的情况下,对低照度隧道环境中的驾驶员的速度感知的影响。试验结果显示:高频视觉信息(2~32 Hz)会使驾驶员显著高估速度,中频视觉信息(0.4~1 Hz)和低频视觉信息(0.1~0.2 Hz)会使驾驶员显著低估速度;高估速度的程度为中尺度信息大尺度信息小尺度信息。因此,保障隧道内行车安全,可从多尺寸多频率的视觉信息的组合设置着手。     

基于紧急制动行为预测的汽车智能制动灯研究

为预防车辆紧急制动造成的追尾碰撞事故,分析跟驰行驶中相邻车辆驾驶人的制动行为过程,提出基于紧急制动行为预测的防追尾碰撞方法。以实际道路采集的驾驶行为数据为基础,定量分析紧急制动、常规制动、加速换挡等行为的加速、制动、离合器踏板的位移变化过程,建立基于加速踏板位移变化率及驾驶操纵动作时序过程的紧急制动预测模型,设计开发汽车智能制动灯。分别在虚拟驾驶平台和实际道路上,对智能制动灯进行测试试验。结果表明,虚拟驾驶试验情况下,使用智能制动灯的车辆被追尾的发生率降低了30%;实际道路测试情况下,制动灯能够有效预测驾驶人的紧急制动行为,先于常规制动灯0.1~0.3 s点亮,提醒后车驾驶人注意安全。     

隧道入口冰雪环境下驾驶员心生理特性研究*

为提高高速公路冰雪环境下隧道入口行车安全水平,探究隧道入口冰雪环境下驾驶员心生理反应特性,依据心生理理论,通过模拟驾驶试验采集驾驶员在冬季晴、雪天气下隧道入口行驶的心率增长率、速度等数据,分析照度变化率、路面摩擦系数、速度对驾驶员心生理的影响规律,明确不同影响因素下隧道入口驾驶员心率增长率变化特征,利用Matlab构建多因素耦合下隧道入口驾驶员心生理反应模型,并设计实车试验验证心生理反应模型有效性。研究结果表明:冰雪环境下隧道入口驾驶员心率增长率紧张阈值为28%,隧道入口冰雪环境下照度变化率安全阈值为51%,心生理反应模型误差小于10%,吻合程度较好。研究结果对提高山区高速公路隧道入口冰雪环境的行车安全性、降低隧道入口事故风险具有重要意义。     

基于驾驶员心率和速度差的高速公路纵坡度研究*

为减少高速公路纵坡路段的事故发生率,利用心生理检测仪采集驾驶员心率数据,使用V-Box采集道路高程和车辆实时速度数据,采用偏相关分析确定影响驾驶员心率增长率和速度差的显著性因素,分别构建驾驶员心率增长率和速度差与坡度之间的关系模型,通过心率增长率阈值和运行速度协调性,从驾驶舒适性角度确定安全坡度范围。研究结果表明:在纵坡路段行车时,驾驶员心率增长率随坡度增大而增加,速度差随坡度增大而增大,坡长与驾驶员心率增长率和速度差呈弱相关;设计车速为100 km/h的高速公路,上坡坡度应小于等于3.7%,下坡坡度应小于等于3.3%,因此建议纵坡坡度小于等于3.3%为宜。研究结果可为高速公路交通安全和人性化设计提供理论依据。     

The effect of driver eye height on speed choice, lane-keeping, and car-following behavior: results of two driving simulator studies

OBJECTIVE: Two simulator studies were conducted that assessed the effect of driver eye height on speed choice, lane-keeping, and car-following behavior. The effect of eye height on the subjective variables of mental workload, frustration, and confidence was also investigated, as was the contribution of drivers' aggression. METHODS: A total of 43 participants drove a simulated route while seated at two different eye heights: one that represented the view of the road from a large SUV and one that represented the view of the road from a small sports car. Driving scenarios were comprised of both open road and car-following segments. Dependent variables included driver-selected speed, speed variability, lane position, following distance to a slower-moving lead vehicle, and the subjective variables of frustration, confidence, and mental workload. RESULTS: When viewing the road from a high eye height, drivers drove faster, with more variability, and were less able to maintain a consistent position within the lane than when viewing the road from a low eye height. Driver eye height did not influence following distance to a slower-moving lead vehicle. Driver aggression had no effect on any of the dependent variables except level of frustration. CONCLUSIONS: The two studies demonstrate that, when they are not able to reference a speedometer, drivers choose to drive faster when they view the road from an eye height that is representative of a large SUV compared to that of a small sports car. There is a need to educate drivers of SUVs and other tall vehicles of this perceptual phenomenon in order to prevent collisions that may occur in conditions where it is impossible for drivers to base their speed selection solely on posted speed limits, such as in inclement weather.     

Deterioration of the useful visual field with ageing during simulated driving in traffic and its possible consequences for road safety

A previous study has shown that the useful visual field deteriorates in a simulated road traffic situation as a function of the driver’s age and of the vehicle’s speed under monotonous conditions [Rogé, J., Pébayle, T., Lambilliotte, E., Spitzenstetter, F., Giselbrecht, D., Muzet, A., 2004. Influence of age, speed and duration of monotonous driving task in traffic on the driver’s useful visual field. Vision Research 44 (23), 2737–2744]. The aim of this new experiment is to study the effects of traffic density and age on the useful visual field of the driver during a simulated driving task with controlled traffic characteristics (speed, number of cars) for all participants. In total, 10 young drivers (m = 28.2 years) and 10 older drivers (m = 51.2 years) followed a car in road traffic at an average speed of 126 km h⁻¹ during two 2 h sessions corresponding to two conditions of traffic (light traffic, with five vehicles around the participant; and heavy traffic, with nine vehicles). While following this vehicle, the driver had to detect changes in the colour of a signal located in the central part of his or her visual field and a signal that appeared at different eccentricities on the rear lights of other vehicles in the traffic. Analysis of the data indicated that age interacted with the location of the peripheral signal and density of traffic interacted with the duration of driving. The implications of these results are discussed in terms of road safety and in terms of models of deterioration of the useful visual field (general interference and tunnel vision).     

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。     

Fatal crash between a car operating with automated control systems and a tractor-semitrailer truck

Abstract

Objective: The objective of this investigation was to evaluate the interaction between an SAE level 2 automated vehicle and the driver, including the limitations imposed by the vehicle on the driver.

Methods: A case study of the first fatal crash involving a vehicle operating with an automated control system was performed using scene evidence, vehicle damage, and recorded data available from the vehicle, and information from both drivers, including experience, phone records, computer systems, and medical information, was reviewed.

Results: System performance data downloaded from the car indicated that the driver was operating it using the Traffic-Aware Cruise Control and Autosteer lane-keeping systems, which are automated vehicle control systems within Tesla’s Autopilot suite. As the car crested the hill, a tractor trailer began its left turn onto a crossing roadway. Although reconstruction of the crash determined that there was sufficient sight distance for both drivers to see each other and take action, neither responded to the circumstances leading to the collision. Further, based on the speeds of the vehicles and simulations of the truck’s path, the car driver had at least 10.4?s to detect the truck and take evasive action. Neither the car driver nor the Autopilot system changed the vehicle’s velocity.

?At the time of the crash, the system performance data indicated that the last driver interaction with the system was 1?min 51?s prior when the cruise control speed was set to 74?mph. The driver was operating the vehicle using the Autopilot system for 37 of the 41?min in the last trip. During this period, the vehicle detected the driver’s hands on the steering wheel for a total of 25?s; each time his hands were detected on the wheel was preceded by a visual alert or auditory warning.

Conclusions: The National Transportation Safety Board (NTSB) determined that the probable cause of the Williston, Florida, crash was the truck driver’s failure to yield the right of way to the car, combined with the car driver’s inattention due to overreliance on vehicle automation, which resulted in the car driver’s lack of reaction to the presence of the truck. Contributing to the car driver’s overreliance on the vehicle automation was the car’s operational design, which permitted the driver’s prolonged disengagement from the driving task and his use of the automation in ways inconsistent with guidance and warnings from the manufacturer.     

Assessing driver behavior using shrp2 adverse weather data

IntroductionThis study explored how drivers adapt to inclement weather in terms of driving speed, situational awareness, and visibility as road surface conditions change from dry to slippery and visibility decreases. The proposed work mined existing data from the SHRP 2 NDS for drivers who were involved in weather-related crash and near-crash events. Baseline events were also mined to create related metadata necessary for behavioral comparisons. Methods: Researchers attempted, to the greatest extent possible, to match non-adverse-weather driving scenarios that are similar to the crash and near-crash event for each driver. The ideal match scenario would be at a day prior to the crash during non-adverse weather conditions having the same driver, at the same time of day, with the same traffic level on the same road on which the crash or near-crash occurred. Once the matched scenarios have been identified, a detailed analysis will be performed to determine how a driver’s behavior changed from normal driving to inclement-weather driving. Results: Data collected indicated that, irrespective of site location (i.e., state), most crashes and near-crashes occurred in rain, with only about 12% occurring in snowy conditions. Also, the number of near-crashes was almost double the number of crashes showing that many drivers were able to avoid a crash by executing an evasive maneuver such as braking or steering. Conclusions: Most types of near crashes were rear-end and sideswipe avoidance epochs, as the drivers may have had a difficult time merging or trying to change lanes due to low visibility or traffic. Hard braking combined with swerving were the most commonly used evasive maneuvers, occurring when drivers did not adjust their speeds accordingly for specific situations. Practical applications: Results from this study are expected to be utilized to educate and guide drivers toward more confident and strategic driving behavior in adverse weather.     

基于事故分析的公路限高门架警告标志优化设置*

为优化设置公路限高门架警告标志,利用Kruskal-Wallis检验与二元logistic回归分析,从工效学及人因工程学角度剖析事故原因,基于大型车辆驾驶员视认特征与道路交通环境特点,提出警告标志前置设置最低要求,构建考虑大型车辆驾驶员视认角度标志前置距离模型,并提出预防措施。结果表明:限高门架高度警示标志设置不规范,导致停车安全距离不足,标志视认能力下降;根据模型得出公路限高门架警告标志前置参考距离,验证模型有效性,并有针对性提出限高门架警示标志优化设置意见。研究结果可为公路限高门架警告标志设置提供参考和借鉴。     

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.     

基于知信行理论的驾驶人风险行为研究

为研究影响驾驶人风险行为的内在机制,基于知信行(KAP)理论,引入个性特质变量,从社会心理学的角度对驾驶人风险行为进行研究。通过问卷调查方法、探索性因素分析及验证性因素分析,探讨驾驶人风险认知、风险态度及人格特质对驾驶人风险行为的影响,构建驾驶人风险行为模型。结果表明:风险认知、风险态度对风险行为显著正相关,感觉寻求人格特质能直接影响驾驶人的风险行为,也能通过风险认知和风险态度的中介作用对风险行为产生影响。校正驾驶人对风险的认知与态度能够干预并改变驾驶人的风险行为。