Accounting for human-related unobserved heterogeneity in the safety performance of connected vehicles: An incorporation of Bayesian hierarchical negative binomial into simulated work zone warning application

Connected Vehicle (CV) notifications enhance drivers' situational awareness, where, depending on traffic flow characteristics, CV drivers might require sufficient distance and time to adapt their driving behavior accordingly. Hereof, in addition to CVs Market Penetration Rate (MPR) and traffic flow characteristics, humans are still in the safety loop. Hence, there might be a human-related unobserved heterogeneity affecting the safety performance of CVs. A conflation of microsimulation and advanced statistical modeling was followed to account for this unobserved heterogeneity by investigating the random effect of varying delivery distances, specifically provided for the Work Zone Warning (WZW) application. CVs behaviors were calibrated based on the results of driving simulator experiments. A typical WZ was simulated under VISSIM using three levels of low, moderate, and high traffic volumes. Five levels of CV-MPR received the WZW on four different settings of delivery distances, including 1500, 3000, 4500, and 6000 ft, upstream of the Transition Area. Accordingly, 60 scenarios were simulated, and the number of critical Time-to-Collision (TTC) was considered as the response count variable per scenario. Four hierarchical Negative Binomial regressions under Bayesian inference were conducted on 60 observations over four WZ areas, where the delivery distance was treated as a random factor. Analysis of the Intraclass Correlation Coefficient showed that the number of observed critical TTCs is nested within the delivery distances. It was ascertained, at any CV-MPR, when the WZW delivery distance increases, the number of critical TTCs decreases that indicates the same safety performance at higher CV-MPR can be achieved even at lower MPR if the WZW distancing increases. Besides, the optimal Road Side Unit (RSU) placement for WZW was found to be 4500 ft upstream of the Transition Area regardless of traffic volume and CV-MPR. Although this study was restricted to the CV-WZW application, the introduced delivery distance for CV notifications should be regarded in future studies as a contributing factor to the safety assessment of other CV applications.     

Normal and risky driving patterns identification in clear and rainy weather on freeway segments using vehicle kinematics trajectories and time series cluster analysis

Enhancing traffic safety on freeways is the main goal for all transportation agencies. However, to achieve this goal, many analysis protocols of network screening models need to be improved through considering human factors while analyzing traffic data. This paper introduces one on the new analysis protocol of identifying and discriminating between normal and risky driving in clear and rainy weather. The introduced analysis protocol will consider the effect of human factors on updating the networking screening process of identifying hotspots of crash risk. This paper employs the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) data to investigate the behavior of normal and risky driving under both rainy and clear weather conditions. Near-crash events on freeways, which were used as Surrogate Measure of Safety (SMoS) for crash risk, were identified based on the changes in vehicle kinematics, including speed, longitudinal and lateral acceleration and deceleration rates, and yaw rates. Through a trajectory-level data analysis, there were significant differences in driving patterns between rainy and clear weather conditions; factors that affected crash risk mainly included driver reaction and response time, their evasive maneuvers such as changes in acceleration rates and yaw rates, and lane-changing maneuvers. A cluster analysis method was employed to classify driving patterns into two clusters: normal and risky driving condition patterns, respectively. Statistical results showed that risky driving patterns started on average one second earlier in rainy weather conditions than in clear weather conditions. Furthermore, risky driving patterns extended in average three seconds in rainy weather conditions, while it was two seconds in clear weather conditions. The identification of these patterns is considered as a primary step towards an automated development that would distinguish between different driving patterns in a Connected Vehicle CV environment using Basic Safety Messages (BSM) and to enhance the network screening analysis for increased crash risk hotspots.     

欧美日智能网联汽车准入管理研究及启示

智能网联汽车是汽车产业转型升级的重要战略方向,良好的政策环境是其快速发展的关键保障。本文以智能网联汽车准入管理为研究重点,首先对欧美日智能网联汽车准入管理政策、法规和标准进展进行研究,分析主要进展和发展趋势;在此基础上,进一步梳理提出智能网联汽车准入管理面临的自动驾驶安全、责任判定、软件升级、网络安全、标准支撑5项主要挑战;最后,针对挑战提出管理建议。     

基于驾驶员信息处理能力的高速公路作业区限速值计算方法

基于人体工程学原理,将驾驶员在高速公路作业区行驶过程中接触的交通信息分为道路几何信息、道路交通运行信息、交通管理控制信息、气候与环境信息及驾驶员自身信息,将作业区各种道路交通信息进行量化处理,在系统分析驾驶员对信息处理模式的基础上,提出了基于驾驶员信息处理能力的高速公路作业区车速速度限制值计算模型。以2006年5月哈大公路入口作业区为例,考虑车道宽度、开放车道数、隔离设施、合流机会、合流冲突及大车影响对驾驶员接受信息的量化影响,计算得出该作业区的限速值为50 km/h,与工程实际中采用的55 km/h吻合较好,验证了本文所提方法的有效性,为高速公路交通管理部门确定车辆速度限制标准提供了一种参考方法。     

某重卡后视镜视野分析及法规校核

汽车外后视镜是车辆主动安全防御性的重要设备。有经验的驾驶者都知道,后视镜性能不佳(存在盲区或视野宽度不足)会严重影响驾驶安全。为保证驾驶员在操作过程中,得到充足的后视镜视野反馈信息,提高车辆行驶过程中的安全性,对某重卡汽车的外后视镜进行视野分析及法规校核。     

网联环境下高速公路辅助驾驶车辆编队评估

为评估智能网联环境下高速公路辅助驾驶车辆编队的效果，首先基于V2X （Vehicle to Everything）和智能驾驶人模型（Intelligent Driver Model，IDM）对网联环境下的车辆跟驰行为进行建模，并对其进行参数校准；其次从安全性评价指标和通行效率两方面构建编队效果评价体系；然后通过VISSIM和VBA联合仿真，改变编队的车道、交通流量、网联车渗透率等变量进行试验。仿真结果表明，网联环境下车辆辅助驾驶编队在不同层面对于安全性与效率性都有提升；最后以不同期望速度在网联环境和非网联环境下分别进行实车辅助驾驶编队试验，以验证评价指标体系以及仿真试验的有效性。其中，实车试验结果显示，期望速度为70 km·h^-1时，网联环境下的辅助驾驶编队通行效率比非网联环境提升56%，90 km·h^-1时提升37.2%，110 km·h^-1时提升39.8%。通过与仿真试验结果对比，表明网联环境下车辆辅助驾驶编队对交通流安全性有一定程度的提升。     

Investigating the attitudes of Egyptian drivers toward traffic safety

One of the main pillars for improving road safety in any country is a good understanding of traffic safety culture and the driving behavior of local drivers. The primary aim of this study was to determine whether Egyptian drivers differ in traffic safety attitudes and level of acceptance of risky driving behavior. A questionnaire survey was conducted on the driving cognition of the participants. An exploratory factor analysis was used to assess the number of factors that differentiated the three types of drivers. Then a hierarchical cluster analysis was performed to group the drivers with similar patterns of scores on the factors into clusters. Three driver clusters emerged: The drivers in cluster 1 were “drivers who rigidly followed regulations” (51.7%). The drivers in cluster 2 were “drivers who violated safety precautions” (23.3%). The drivers in cluster 3 were “drivers who had a tendency to violate regulations” (25.0%). A similarity between the social norms and personal attitudes of drivers was found. This can be explained by the high social norm of violating traffic laws, which can lead to more drivers accepting violations. The majority of the older drivers and drivers with no violations or traffic accident on their record in the past 2 years were in cluster 1. Cluster 2 had the highest proportion of young drivers who wore their seat belts and used hands-free phones while driving. Cluster 3 drivers accepted very dangerous violations, such as texting while driving, driving while intoxicated, and driving at very high speeds. They reported significantly more traffic accidents, but no more violations than the other two clusters. The results of this study can be used to improve road safety programs for education and enforcement in Egypt.     

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。     

汽车驾驶员不安全行为评价分析

为稳步增强汽车驾驶的安全性,减少安全事故的发生,驾驶人员除了需要具备足够的应急应变能力之外,还需要做好安全行为习惯养成工作,形成良好的驾驶习惯。文章以汽车驾驶员不安全行为作出研究对象,在明确不安全行为表现的基础上,深刻分析影响汽车驾驶人员不安全行为的因素,在此基础上,制定合理的应对策略,旨在引导驾驶人员形成安全行为,以不断提升驾驶员的驾驶能力。     

Comparative study between Korea and UK: relationship between driving style and real-world fuel consumption

It is known that differences in driving styles have a significant impact on fuel efficiency and driving styles are affected by various factors such as driver characteristics, street environment, traffic situation, vehicle performance, and weather conditions. However, existing knowledge about the relationship between driving style and fuel consumption is limited. Thus, the aim of this study was to analyze the relationship beteen driving style and fuel consumption. The analysis presented in this paper used data from three on-road experiments were conducted independently in two different countries, i.e. South Korea and the United Kingdom. In this study, 91 participants, consisting 44 UK drivers and 47 Korean drivers, were asked to drive approximately 28 km of UK road and 21 km of Korean road, respectively. Driving data, including real-time fuel consumption, vehicle speed, and acceleration pedal usage were collected. The results suggested that driving styles including average vehicle speed and average throttle position were highly correlated with the real-world fuel consumption, and the cultural factors, e.g. road environment, traffic design, and driver’s characteristics affected the driving styles and, consequently, fuel efficiency.     

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...     

关于公交车驾驶员安全驾驶视觉特性的分析

随着我国现代化的不断发展,城镇居民的生活水平不断提高,对各种交通工具的需求也在不断加大。同时,由于环境的逐渐恶化和人们环保意识的提高,越来越多人在出行时选择公交或者地铁等公共交通工具出行,因此,公交车驾驶的安全问题受到大家广泛关注。相关数据表明,驾驶员的违规驾驶是引起交通事故的重要原因,应从驾驶员的角度出发,分析公交车驾驶员安全与驾驶视觉特性的关系,有效保障公交车行驶的安全性。公交车驾驶员是驾驶行为的参与者,行驶过程中结合视觉信息操作驾驶行为。本文简要概述了驾驶员的视觉基本特性与疲劳驾驶、分心驾驶及驾驶经验对视觉特性的影响等方面,基于驾驶员的视觉特性影响因素进行研究和提出几点建议。     

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。     

Exploring factors contributing to injury severity at work zones considering adverse weather conditions

Despite recent efforts to improve work zone safety, the frequency and severity of crashes at work zones are still considerably high. The effect of work zones on traffic safety can be exacerbated by adverse weather conditions. As an example, a sudden reduction in visibility may intensify the severity of work zone crashes. There is a lack of studies that strive to gain a good understanding of the effect of weather on the severity of work zone crashes. In this study, an Ordered Probit Model was developed to identify factors affecting the severity of work zone crashes in different spatial, temporal, and environmental conditions in Washington state using five-year of work zone-related crashes (2009–2013). The interesting findings of this study showed that weather and lighting conditions are among the most important factors influencing the severity of crashes at work zones. Lack of daylight was found to be a determining factor in increasing the severity of work zone crashes, specifically, during dusk and dawn. It was also found that although drivers have less severe work zone-related crashes in adverse weather conditions, the interactions between adverse weather conditions and other contributing factors might increase the severity of work zone crashes. The results of this study will help traffic engineers to design effective safety countermeasures considering different contributing factors including the weather and lighting conditions in the work zone planning and installation stages to prevent safety deficiencies.     

驾驶员自身因素引起的驾驶疲劳对交通安全的影响

为更好地评价驾驶员自身因素引起的驾驶疲劳,主要对6个方面的因素进行了分析。运用层次分析法建立评价驾驶疲劳的数学模型,通过综合评价来确定驾驶员的安全度。以期对相关工作提供参考。     

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。     

Influencing driver chosen cornering speed by means of modified steering feel

This paper presents an investigation about influencing the driver's behaviour intuitively by means of modified steering feel. For a rollover indication through haptic feedback a model was developed and tested that returned a warning to the driver about too high vehicle speed. This was realised by modifying the experienced steering wheel torque as a function of the lateral acceleration. The hypothesis for this work was that drivers of heavy vehicles will perform with more margin of safety to the rollover threshold if the steering feel is altered by means of decreased or additionally increased steering wheel torque at high lateral acceleration. Therefore, the model was implemented in a test truck with active steering with torque overlay and used for a track test. Thirty-three drivers took part in the investigation that showed, depending on the parameter setting, a significant decrease of lateral acceleration while cornering.     

Assessing the effect of inattention-related error and anger in driving on road accidents among Iranian heavy vehicle drivers

Extensive studies have examined the effect of variables such as demographic characteristics, insomnia, and working conditions of drivers individually on inattention-related error as well as expression of anger in driving. Nevertheless, so far no study has tested the concurrent effect of these factors on crashes. This study has dealt with indirect investigation of the effect of variables including demographic characteristics, insomnia, and working conditions of drivers on inattention-related error and expression of anger in driving (as mediation model). Next, the effect of these two variables on the probability of incidence of road crashes has been assessed among truck drivers. For this purpose, 780 Iranian truck drivers were interviewed by validated questionnaires including insomnia severity index (ISI), attention related driving errors scale (ARDES), and driving anger expression (DAX). To confirm the validity of these questionnaires, confirmatory factor analysis (CFA) was used. Next, structural equation modeling (SEM) was employed for investigating the relationships between these latent variables and truck drivers' demographic characteristics, working conditions and their crash involvement. SEM results indicated that as the severity of insomnia among drivers increased, they committed more inattention-related errors, and also expressed more anger during driving. Generally, the results of this study indicated that with increase in the extent of experience and safety knowledge of drivers and improvement in their working conditions and their sleep status, it is possible to reduce inattention-related error and expression of anger during driving among heavy vehicle drivers.     

Driver behavior formulation in intersection dilemma zones with phone use distraction via a logit-Bayesian network hybrid approach

Use of cellular phone while driving is one of the top contributing factors that induce traffic crashes, resulting in significant loss of life and property. A dilemma zone is a circumstance near signalized intersections where drivers hesitate when making decisions related to their driving behaviors. Therefore, the dilemma zone has been identified as an area with high crash potential. This article utilizes a logit-based Bayesian network (BN) hybrid approach to investigate drivers' decision patterns in a dilemma zone with phone use, based on experimental data from driving simulations from the National Advanced Driving Simulator (NADS). Using a logit regression model, five variables were found to be significant in predicting drivers' decisions in a dilemma zone with distractive phone tasks: older drivers (50–60 years old), yellow signal length, time to stop line, handheld phone tasks, and driver gender. The identified significant variables were then used to train a BN model to predict drivers' decisions at a dilemma zone and examine probabilistic impacts of these variables on drivers' decisions. The analysis results indicate that the trained BN model was effective in driver decision prediction and variable influence extraction. It was found that older drivers, a short yellow signal, a short time to stop line, nonhandheld phone tasks, and female drivers are factors that tend to result in drivers proceeding through intersections in a dilemma zone with phone use distraction. These research findings provide insight in understanding driver behavior patterns in a dilemma zone with distractive phone tasks.     

探秘东风天龙旗舰

从2013年4月上海车展首度发布,到2014年4月北京车展再度亮相,东风天龙旗舰依然保持着高度的神秘感。虽然底盘部分不再被遮盖,但锁闭的驾驶室以及黑色的车窗仍然让人无法深入了解这款车型。