基于安全车速差的高速公路特长隧道环境驾驶人行为风险特性研究

为研究高速公路特长隧道环境下驾驶人行为风险特性，选取2座典型特长隧道进行实车试验，通过采集熟练驾驶人和非熟练驾驶人的速度数据，将此作为主观预期车速，结合道路行车环境的客观安全车速，构建基于安全车速差的驾驶人行为风险量化方法。在划分隧道路段为入口段、行车段和出口段的基础上，通过切分行车区间，对比分析出入口段2类驾驶人行为风险变化特性及整个隧道路段和普通高速路段的行为风险变化曲线。结果表明： 1)在隧道内部，相对于非熟练驾驶人，熟练驾驶人表现出更高的行为风险值；在隧道外部，则非熟练驾驶人的行为风险值更高一些。2)所有类型驾驶人在普通高速路段行为风险值最高，在隧道入口段的行为风险值最低。上述结果说明： 在隧道路段，熟悉试验道路的驾驶人车速行为并不安全，行为风险值相对较高。     

Driver propensity characterization for different forward collision warning times

The forward collision warning system, which warns danger to the driver after sensing possibility of crash in advance, has been actively studied recently. Such systems developed until now give a warning, regardless of driver’s driving propensity. However, it’s not reasonable to give a warning to every driver at the same time because drivers are different in driving propensity. In this study, to give a warning to each driver differently, three metrics classifying driver’s driving propensity were developed by using the driving data on a testing ground. These three metrics are the predicted time headway, required deceleration divided by the deceleration of the leading vehicle, and the resultant acceleration divided by the deceleration of the leading vehicle. Driving propensity was divided into 3 groups by using these metrics for braking and steering cases. In addition, these metrics were verified by making sure that braking propensity could be classified on public roads as well.     

邻车切入工况下前撞预警系统的驾驶人依赖特性

为提升邻车切入工况下的行车安全,基于驾驶模拟实验平台,研究了驾驶人对前撞预警系统的依赖特性评价方法以改进预警系统的设计。以预警时机(即碰时间TTC)为研究变量,采集了12名驾驶人的实验数据,以制动依赖指数、次任务评分为2项客观指标,以危险度评分、信任度评分为2项主观指标,建立了评价体系模型,实现了对驾驶人系统依赖程度的量化评价。设计了L9(34)正交实验,建立了依赖特性评价回归模型。结果表明:预警时机(TTC)对依赖特性的影响最为显著:过晚的预警时机(TTC=2.4 s)降低系统的有效性;过早的预警时机(TTC=1.2 s)易导致驾驶人对系统过度依赖。因而,适度推迟预警时机(TTC=1.8 s)可以抑制依赖性的产生,提升系统的安全性。     

Assessment of the effectiveness of connected vehicle weather and work zone warnings in improving truck driver safety

Improving work zone safety remains a prime challenge for the transportation sector in the United States. In particular, the frequency and severity of work zone crashes involving large trucks in rural freeways are alarming. Lack of compliance with the instructions provided at work zones results in increased crash risk. In-vehicle advanced warning systems enabled by Connected Vehicle (CV) technology have the potential to prompt appropriate driver response, make navigation more predictable, and improve overall work zone safety. This study falls under the umbrella of the WYDOT Connected Vehicle Pilot Program and seeks to investigate the impacts of the Pilot's real-time weather and work zone notifications on the behavior of truck drivers in rural freeway work zone settings under poor visibility. Twenty professional truck drivers participated in this simulator study. The driving scenarios were designed to mimic the driving conditions experienced on Wyoming Interstate 80. Findings suggest that exposure to the CV notifications has promising safety benefits manifested in improved driver behavior and response. Furthermore, both the weather and work zone notifications acquired high approval from the participants in terms of usefulness and ease of understanding. Nonetheless, the display of multiple work zone warnings on the Human Machine Interface may had introduced little to moderate distraction for some participants. Overall, this study brings forth valuable lessons that are being funneled to support informed decision making to enhance the Pilot's existing Human Machine Interface design.     

Intelligent speed adaptation: Preliminary results of on-road study in Penang,Malaysia

The first field experiment with intelligent speed adaptation (ISA) in Malaysia was held in December 2010 in the State of Penang. Eleven private cars were instrumented with an advisory system. The system used in the present study included a vocal warning message and a visual text message that is activated when the driver attempts to exceed the speed limit. When the driver decreases the speed, the warning stops; otherwise it is continuously repeated. The test drivers drove the vehicles for three months with the installed system, and the speed was continuously logged in all vehicles. The warning was however only activated in the second month of the three month period. The present study aimed to evaluate the effects of an advisory ISA on driving speed, traffic safety, and drivers' attitude, behavior, and acceptance of the system. To examine these effects, both the survey and the logged speed data were analyzed and explored. The results show a significant reduction in the mean, maximum and 85th percentile speed due to the use of the system. However, there was no long-lasting effect on the speed when the system was deactivated. In the post-trial survey, drivers declared that the system helped them well in following the speed limits and that it assisted them in driving more comfortably. Furthermore, the warning method was more accepted compared to a supportive system, such as active accelerator pedal (AAP). After the trial, most drivers were willing to keep an ISA system.     

基于驾驶员跟车习惯的报警/避撞算法研究

在驾驶员实车实验的基础上,研究跟车工况中的驾驶员行为特性和习惯,建立驾驶员跟随车距模型,并结合对车辆制动过程的分析,研究分别基于驾驶员制动行为特性和驾驶员跟随车距模型的报警/避撞算法,通过改变算法的参数值,可使算法得到的报警/避撞时机符合不同驾驶员的驾驶习惯。利用实验数据对算法进行离线检验,验证该算法在报警/避撞系统中的适用性。     

Human-Centered Design of an Acc-With-Braking and Forward-Crash-Warning System

This paper addresses the development of driver assistance systems whose functional purposes are to provide both adaptive cruise control (ACC) and forward collision warning (FCW). The purpose of the paper is to combine concepts from human factors psychology, vehicle-dynamics, and control theory, thereby contributing to the body of knowledge and understanding concerning human-centered approaches for designing and evaluating driver assistance systems. Conceptual and experimental results pertaining to driving manually and with the assistance of ACC and FCW are presented. The following human-centered aspects of driver-assistance systems are analyzed and presented: the looming effect; including rule-based and skill-based behavior in the design of ACC systems; using desired dynamics in controlling the driving process; braking rules that trade headway range for deceleration level; and collision-warning rules based on two different stress indicators. Field-test data are examined to justify and verify the parametric values selected for use in human-centered ACC systems. Measured data from on-road driving are used to evaluate the performance of proposed FCW systems in braking situations. The paper concludes with observations concerning the difficulty of developing a clear understanding of when and why drivers brake.     

Human-Centered Design of an Acc-With-Braking and Forward-Crash-Warning System

This paper addresses the development of driver assistance systems whose functional purposes are to provide both adaptive cruise control (ACC) and forward collision warning (FCW). The purpose of the paper is to combine concepts from human factors psychology, vehicle-dynamics, and control theory, thereby contributing to the body of knowledge and understanding concerning human-centered approaches for designing and evaluating driver assistance systems. Conceptual and experimental results pertaining to driving manually and with the assistance of ACC and FCW are presented. The following human-centered aspects of driver-assistance systems are analyzed and presented: the looming effect; including rule-based and skill-based behavior in the design of ACC systems; using desired dynamics in controlling the driving process; braking rules that trade headway range for deceleration level; and collision-warning rules based on two different stress indicators. Field-test data are examined to justify and verify the parametric values selected for use in human-centered ACC systems. Measured data from on-road driving are used to evaluate the performance of proposed FCW systems in braking situations. The paper concludes with observations concerning the difficulty of developing a clear understanding of when and why drivers brake.     

人-车-路交互下的驾驶人风险响应度建模

人机共驾中，共驾模式的选择和驾驶控制权的分配高度依赖于对驾驶人状态的正确识别。为了分析人机共驾中驾驶人的状态，对行车风险场模型进行重构，通过构建风险场力作用机制，建立包含驾驶人特性、自车特性和外部风险特性的人-车-路闭环系统中的驾驶人风险响应度模型，用于表征驾驶人对风险的认知能力和应对倾向。根据24位驾驶人在跟车和并道2个场景中的驾驶试验结果，对不同风险响应度下驾驶人的驾驶特性进行分析。研究结果表明：驾驶人风险响应度在驾驶过程中具有时变性，在驾驶人个体之间和不同驾驶场景间均存在差异性。在风险响应度分别为低、中、高的3类驾驶片段中，驾驶人在驾驶时的碰撞时间倒数T_TCi和加减速行为均具有显著差异（p<0.05）；风险响应度较高的保守型驾驶中，驾驶人行车时倾向于保持较小的T_TCi（均值为-0.48 s^-1，标准差为1.25 s^-1），单位时间内制动操作最多[均值为0.65次·（15 s）^-1]，总体驾驶风格倾向于规避风险；风险响应度较低的激进型驾驶中，驾驶人行车时倾向于保持最大的T_TCi（均值为0.28 s^-1，标准差为0.42 s^-1），相较于保守型驾驶，单位时间内加速操作较多[均值为0.48次·（15 s）^-1]，制动操作较少[均值为0.50次·（15 s）^-1]，总体驾驶风格倾向于追求行驶效率；风险响应度居中的平衡型驾驶中，驾驶人行车时所保持的T_TCi居中（均值为0.04 s^-1，标准差为0.36 s^-1），单位时间内加速操作[均值为0.23次·（15 s）^-1]和制动[均值为0.41次·（15 s）^-1]操作总数最少，对于行驶效率和行车安全的追求相对均衡。相较于以往将驾驶人作为孤立个体的驾驶人状态评估方法，所提出的驾驶人风险响应度模型可以依据驾驶人在人-车-路交互中的驾驶表现，更为全面地反映驾驶人的个性化驾驶状态。     

普通公路驾驶员期望车速调查分析与建模

为了解普通公路驾驶员的车速选择机理,对驾驶员进行了期望车速问卷调查,对驾驶员的个人信息、车辆特征、以及驾驶员对超速10%的认可态度等可能影响期望车速的因素进行了重点调查。根据限速值的不同,分80、60和40 km/h 3个等级进行了调查,并为各等级公路选取了代表路段,由具有类似驾驶经历的驾驶员认真填写。对435份有效问卷进行了分析,发现期望车速广泛存在于驾驶员心中,驾驶员在行车过程中会使车速尽量维持在期望车速附近。期望车速主要受限速值、驾驶员的驾龄、对超速10%的态度以及车辆性能等因素的影响,驾驶员的性别和年龄对期望车速的影响不显著。采用逐步回归法,得到了期望车速的计算方法。对期望车速的特点、交通安全措施以及调查方案中可供改进的地方进行了总结。     

考虑驾驶人风格的跟车预警规则研究

为研究驾驶人的跟车特性及探究可适用于不同风格驾驶人的跟车预警规则,为自动驾驶车辆开发可满足不同用户驾驶需求和驾乘体验的主动安全预警系统,选取50名被试驾驶人开展实车试验,采集驾驶人跟车行为表征参数并基于雷达数据确定跟车事件提取规则。选取平均跟车时距和平均制动时距为二维向量,使用基于K-means聚类结果的高斯混合模型将驾驶人聚类为3种风格类型(冒进型、平稳型、保守型)。通过分析3组驾驶人的跟车及制动数据,将不同类型驾驶人的制动时距分位数作为跟车预警阈值,结合实际预警数据及不同制动时距分位数对应的预警正确率,对现有跟车预警规则进行调整,以适应不同类型驾驶人的驾驶需求。研究结果表明:3组驾驶人的平均跟车时距和平均制动时距差异显著,冒进型驾驶人倾向于选择较小的跟车时距和制动时距,保守型驾驶人的跟车时距和制动时距则普遍较大;3组驾驶人的实际跟车预警次数为215次,驾驶人采取制动操作而系统未予以预警的次数为329次,系统整体预警正确率为21.9%,漏警率为87.5%,通过分析信息熵等判定当前预警规则并不合理;将每类驾驶人制动时距的10%分位数作为阈值时的预警效果较好,调整后的跟车预警规则能在一定程度上适应不同的驾驶人类型。     

城市快速路匝道合流区车辆交互行为模式

在城市快速路匝道合流区,驾驶任务难度主要来自于车辆与其周边车辆之间的动态交互,目前对这种交互行为的特征和机理的认识还不十分清楚。基于从无人机视频中提取的高精度车辆轨迹数据,提取出表征车辆交互行为的指标TTC和GAP,并结合速度、加速度、车道位置等其他指标,对车辆的交互过程加以刻画,从中获得了大量交互行为实例,并在此基础上归纳总结出9种典型的车辆交互行为模式。通过分析各模式特征发现：即使在相同的外部环境下,车辆交互行为模式也可能存在差异,这表明交互行为不仅与车辆之间的相对位置、时空距离、速度状态等环境因素有关,还与驾驶人的应对能力、动机及风险意识等认知心理有关;另外,不同的交互模式面临的风险不同,并且该风险既可能是周边车辆行为发生改变而被迫卷入,也可能是驾驶人自身主动寻求的结果;9种不同类型的交互行为模式,构成了驾驶人自行感知的4种风险状态互相转换的具体实现形式;在驾驶过程中,驾驶人努力寻找契机并选择某种交互行为模式在各个风险状态之间来回切换,并非仅由心理压力较大的危险态向压力较小的自由态转换,也会发生反向转换,前者主要由降低事故风险和减少认知努力的动机驱动,后者旨在追求行车效率,但同时驾驶人会付出更多的认知努力以对抗风险的增加,这充分反映了驾驶人试图在行车效率、事故风险与认知努力三方面取得平衡。研究成果对深化理解驾驶行为及其背后的决策机制具有积极意义。     

Risk assessment based on driving behavior for preventing collisions with pedestrians when making across-traffic turns at intersections

Traffic accident statistics in Japan show the necessity of preventing vehicle-on-pedestrian accidents. If the risk of a vehicle colliding with pedestrians could be evaluated in advance, driver-assistance systems would be able to support drivers to avoid potential collisions. Here, features of driving behavior and methods for assessing the risk of collision were investigated for a right turn at an intersection in left-hand traffic, which is a typical vehicle-on-pedestrian accident scenario. The results showed that pedestrian-collision risk can be evaluated from how the driver slows the vehicle and where the driver looks while turning during the maneuver. Moreover, pedestrian-collision risk could be predicted based on driving behavior upon commencement of steering when making an across-traffic turn.     

Trip characteristics impact on the frequency of harsh events recorded via smartphone sensors

The Internet of Things (IoT) constantly offers new opportunities and features to monitor and analyze driver behavior through wide use of smartphones, effective data collection and Big Data analysis, resulting in assessment and improvement of driver behavior and safety. The objective of the present study is to investigate the impact of detailed trip characteristics on the frequency of harsh acceleration and harsh braking events through an innovative smartphone application developed within the framework of BeSmart project. A 200-driver naturalistic experiment spanning 12 months is carried out since July 2019. During the first two months, participants were asked to drive in the way they usually did, without receiving any feedback on their driving behavior from the application. Over the subsequent two months, participants were provided with personalized feedback, a trip list and a scorecard regarding their driving behavior, allowing them to identify their critical deficits or unsafe behaviors. Some of the most important risk factors, such as speed and driving above the speed limit, usage of mobile phone while driving and harsh events (acceleration and braking) are recorded through the application and subsequently analyzed. Generalized Linear Mixed-Effects Models were fitted to the trips of car drivers who made frequent trips for both experiment phases in order to model the frequencies of harsh events. Results indicate that maximum speed, the percentage of speeding duration and total trip duration are positively correlated with both harsh acceleration and harsh braking frequencies. On the other hand, the exposure metric of total trip distance was found to be negatively correlated with both harsh event types. A small positive correlation of the percentage of mobile use duration with harsh accelerations was also detected.     

In and out vision-based driver-interactive assistance system

The overall driving environment consists of the Traffic environment, vehicle and driver states (TVD). advanced driver assistance Systems (ADAS) must consider not only information on each of the TVD states but also their context. Recent research has focused on making more efficient and effective assistance systems by fusing all the information from the TVD states. Based on this research trend, this paper focuses on decision-level fusion to estimate the level of danger of a warning by using visual information of the traffic environment and the driver state. The driver state consists of the gazing region and the facial feature points that are obtained using the active appearance model (AAM). The traffic environment state consists of time to collision (TTC), time to lane Crossing (TLC), and lane color information, which are obtained from the environment in front of the vehicle, i.e., position of lanes and other vehicles. Warnings against lane-off, collision, and driver inattention are generated by fusing this vision-based information from inside and outside the vehicle. The experimental results prove that our vision-based interactive driver assistance system reduces most useless warnings.     

Human driving data-based design of a vehicle adaptive cruise control algorithm

This paper presents a vehicle adaptive cruise control algorithm design with human factors considerations. Adaptive cruise control (ACC) systems should be acceptable to drivers. In order to be acceptable to drivers, the ACC systems need to be designed based on the analysis of human driver driving behaviour. Manual driving characteristics are investigated using real-world driving test data. The goal of the control algorithm is to achieve naturalistic behaviour of the controlled vehicle that would feel natural to the human driver in normal driving situations and to achieve safe vehicle behaviour in severe braking situations in which large decelerations are necessary. A non-dimensional warning index and inverse time-to-collision are used to evaluate driving situations. A confusion matrix method based on natural driving data sets was used to tune control parameters in the proposed ACC system. Using a simulation and a validated vehicle simulator, vehicle following characteristics of the controlled vehicle are compared with real-world manual driving radar sensor data. It is shown that the proposed control strategy can provide with natural following performance similar to human manual driving in both high speed driving and low speed stop-and-go situations and can prevent the vehicle-to-vehicle distance from dropping to an unsafe level in a variety of driving conditions.     

自然驾驶状态下重型工程车驾驶人驾驶稳定性分析

重型工程车行驶过程中事故风险大,发生恶性事故的概率高,易造成重大生命和经济损失,其运输安全管理问题面临挑战.为探究重型工程车驾驶人驾驶稳定性与相关影响因素之间的关系,开展重型工程车自然驾驶试验,提取车辆运动学、道路条件、驾驶人状态和工作时间等数据;采用速度均值和速度标准差表征驾驶人驾驶稳定性,以睡眠模式、道路线形、道路...     

风险情境下老年驾驶人行为特性研究

为研究风险情境下老年驾驶人与中青年驾驶人行为特性的差异,并确定老年驾驶人的眼动、心理生理、驾驶操作及风险感知等各类行为特性的衰退情况;选取19位老年驾驶人和19位中青年驾驶人作为试验对象,应用眼动仪、生理仪及驾驶模拟平台开展驾驶模拟试验;采集5种风险场景下2组驾驶人的眼动、心理、生理、操作行为与车辆运行数据;对比分析2组驾驶人的注视及扫视等眼动行为特性、心率变异及皮电等心理生理行为特性、制动及转向等操作行为特性、风险反应及敏感度等风险感知行为特性。试验结果表明：2组驾驶人的各类行为特性均随风险等级的增加呈现一定的规律性变化,随着风险等级的增加,2组驾驶人的注视持续时间、皮电均值及增长率、心率增长率和风险敏感度亦随之增加,而扫视、心率变异指标SDNN、制动时间及风险反应时间等指标随风险等级的增加而下降;上述指标的规律性变化说明驾驶人对风险的关注度和敏感度随着风险自身危险性的上升而不断增加,进而做出的反应也就越早,同时伴随着心理紧张程度增加,需要付出的努力也越大,与年龄的高低无关;另一方面,老年驾驶人的各类行为特性出现明显的衰退且与中青年驾驶人存在显著差异,其中老年驾驶人的注视持续时间、扫视幅度、扫视速度等眼动指标分别衰退了37.83%、27.58%、23.80%,皮电均值、心率增长率和SDNN心理生理指标分别衰退了57.67%、20.08%和29.14%,转向熵、车速控制和制动反应时间操作行为指标分别衰退了32.81%、20.34%和49.48%,风险敏感度、判断阈值和风险反应时间风险感知指标分别衰退了13.70%、8.66%和31.80%。通过对风险情境下老年驾驶人的各类行为特性进行详细分析,确定了老年驾驶人各类行为的衰退情况,对老年驾驶人行为特性的研究具有一定借鉴意义。     

融合预瞄与势场栅格法的紧急避撞驾驶人模型

紧急避障工况下的驾驶人操作具有响应快且动作幅值较大的特点，传统预瞄驾驶人模型已不能适应紧急避障工况的需求，故考虑实际避撞场景开发相应的驾驶人模型就显得尤为必要。针对此种状况，基于驾驶模拟器，结合紧急避撞工况实际驾驶人操纵数据，提出了一种融合预瞄与势场栅格法的紧急避撞驾驶人模型。首先针对紧急避撞工况下车辆运动特点，建立车辆横、纵向耦合非线性动力学模型，并给出其状态空间方程描述；其次，离线仿真分析紧急避撞系统特征，并结合线性二次型最优控制，建立最优曲率预瞄+跟踪误差反馈驾驶人模型；再者，基于紧急避撞工况下真实驾驶人经验转向行为数据，开发基于势场栅格法的驾驶人模型，为进一步提高驾驶人模型对避障行驶工况的适应性，将基于势场栅格法的驾驶人模型与最优曲率预瞄+跟踪误差反馈驾驶人模型进行融合，并基于Sigmoid函数实现两者输出的权重分配；最后，针对所提出的融合预瞄与势场栅格法的驾驶人模型，开展基于避撞台架的驾驶人在环仿真试验以及实车试验。研究结果表明：在紧急避撞工况下，对比最优曲率预瞄+跟踪误差反馈驾驶人模型，融合预瞄与势场栅格法的驾驶人模型输出的转向动作与实际驾驶人行为较为接近，可在保证避障安全性的前提下，兼顾避障路径跟踪精度与车辆行驶的稳定性。     

不同干预方式下右转车辆避让行人效果研究

为探究车辆右转过程中不同干预方式对驾驶人未规避行人行为的改善情况，设计听觉警示、触觉警示、形式惩罚、利益惩罚和道德惩罚5种干预方式，分为控制组、警示组和惩罚组，试验基于眼动仪和模拟驾驶仪展开。定义注视次数、注视点分布信息熵、平均注视时间、视线转移路径、区域关注概率和瞳孔面积6项指标表征驾驶人眼动特性，提取制动踏板深度比例、行车速度2项指标反映车辆运行状态。经方差分析确定各干预方式差异的显著性水平，从注视特性指标、扫视特性指标、瞳孔面积指标、驾驶人制动指标和机动车制动指标5个方面分析不同干预下驾驶人视觉及操纵响应特征，并收集被试反馈的追踪问卷。试验结果表明：不同干预方式对右转车辆未避让行人均有规范作用，各组干预效果由强到弱依次为利益惩罚、道德惩罚、形式惩罚、触觉警示和听觉警示。利益惩罚性主动干预效果优势显著，注视点分布信息熵最高为0.74，右侧平均注视次数为6次，平均注视时间增加至13.25 s，驾驶人对右侧注视概率增加至0.403，瞳孔面积明显增大，制动踏板深度比例维持在0.8，右转车速下降至20 km·h^-1以下，谨慎驾驶程度和避让行人意识均有提升。一致性追踪问卷调查表明，结束试验时32%的驾驶人对利益惩罚印象深刻，驾驶人对其主观认可度高达83%，具有较强的推广性；该干预方式可帮助驾驶人规范驾驶行为，树立避让行人的安全驾驶意识。