Investigating performance of a novel safety measure for assessing potential rear-end collisions: An insight representing a scenario in developing nation

Road safety is one of the major concerns in the ever-growing traffic network. In addressing this, surrogate safety measures play a critical role in identifying collision instincts. Besides the added advantage of quantifying collision instincts in advance, surrogate safety measures have their limitations. For example, in some instances, those measures tend to show erroneous results. In this paper, a new surrogate safety measure Instant Heeding Time (IHT), is presented based on follower vehicle attention in the traffic streams. This new measure is integrated with a distance gap and the vehicles' speeds to assess probable rear-end collisions. Further, along with other safety measures, the developed safety framework is tested over a study section, with the help of trajectory datasets at three traffic flow conditions (free flow, capacity, and congested) under prevailing heterogeneous (mixed) traffic conditions. Based on the safety framework, it is observed that, in the case of free flow and capacity conditions, 23 and 55 probable rear-end collisions points are detected. At the congested conditions, no rear-end collision points are observed. Further, smaller vehicles in the traffic stream are associated with a higher number of rear-end collision instincts than other vehicle categories. The conceptualized safety framework can be applied on a real-time basis for monitoring the safety measures for vehicles in a mixed traffic stream.     

视线遮挡条件下面向弱势道路使用者的避撞策略研究

由于视线障碍物造成的“鬼探头”事故已经成为当前城市道路交通事故的主要类型之一。针对汽车碰撞视线遮挡条件下横穿的弱势道路使用者(VRU)的场景, 设计了1种基于碰撞时间比和安全制动距离的避撞策略, 建立车辆与VRU的交通状态数学模型, 分析“鬼探头”场景下的制动避撞临界距离。结合临界距离和车辆与VRU的碰撞时间比, 将可以避免碰撞的场景分为3种工况, 分别采用不同的制动减速度, 建立自动紧急制动避撞策略。通过Euro NCAP CPNC测试场景对该策略与传统TTC制动算法进行比较分析。结果表明, 在Euro NCAP CPNC测试场景中, 自车利用该避撞策略在理想情况下能够在更高的车速情况下完成避撞; 在不能避免碰撞的高速行驶工况中较传统TTC算法能够更加有效降低碰撞速度, 同时降低事故重伤风险和死亡风险, 提高车辆的安全性。      

基于潜类别Logit模型的两轮车骑行者头部损伤影响因素研究

为研究汽车-两轮车碰撞事故中骑行者头部伤害的影响机理,探究头部损伤致因关系中存在的模式差异与异质性,基于中国交通事故深入研究（CIDAS）数据库中的2 806起两轮车事故,建立潜类别Logit模型。以骑行者头部伤害严重程度为因变量,将驾驶人、车辆、道路、环境和事故碰撞特征的相关因素作为自变量,取显著水平为0.05,建立多项式Logit模型,在此基础上根据拟合优度指标确定最优分组数并构建出潜类别Logit模型。研究结果表明:模型将事故样本划分为2个类别群体,2个群体在参数、变量分布特征和预测概率上存在显著差异,当事故样本具有“两轮车初始速度>30 km/h”、“骑行者碰撞后抛出距离>10 m”等特征时易被归类到类别1,且类别1对应于更严重的头部伤害;骑行者年龄>50岁、汽车类型为商用货车、两轮车类型为摩托车、事故发生在市区外、两轮车初始速度>30 km/h、头部碰撞玻璃和抛出距离>5 m都会增加头部损伤的严重程度;汽车驾驶人行驶意图为停车或变道时,存在造成严重两轮车事故的风险;佩戴头盔会减弱骑行者头部受到的伤害。      

Three-dimensional behaviour simulation of vehicle-to-vehicle collision by dynamic model

Previous studies of safety during vehicle collision pay attention to phenomena in the short time from starting collision, and the behaviour of rollover is studied separately from that at collision. Most simulation of traffic accidents are two-dimensional. Therefore, it is indispensable for vehicle design to analyze three-dimensional and continuous behaviour from crash till stopping. Accordingly, in this study, three-dimensional behaviour of two vehicles at collision is simulated by computer using dynamic models. Here, by comparison of the calculated results with real vehicles' collision test data, it is confirmed that the dynamic model of this study is reliable. It was confirmed in this study that the dynamic model thus established was applicable to various types of collisions and vehicles.     

车身焊接修复过程中影响焊接质量的因素

随着科技日益发展,人们的生活水平越来越好.汽车逐步成为家庭的出行的必备工具.我们中国是人口大国,汽车的保有量逐年上升,伴随车辆的不断增多,汽车发生的碰撞事故也随之增多.而对于严重碰撞损伤的车辆,车身修复过程中的焊接质量,直接影响着维修后车辆的安全系数.     

On the overriding issue of train front end collision in rail vehicle dynamics

A three-dimensional dynamic model of crashed vehicles coupled with moving tracks is developed to research the dynamic behaviour of the train front end collision on tangent tracks. The three-dimensional dynamic model consists of a crashed vehicle model, moving track models, a simple wheel–rail contact model, a velocity-based coupler model and the model of energy absorption and anti-climbing devices. The vector method dealing with the nonlinear wheel–rail geometry is put forward in the paper. The developed model is applicable in the scope that central collisions occur on tangent tracks at low speeds. The examples of the vehicle impacting with a rigid wall and the train front end collision are carried out to obtain the dynamic responses of vehicles. The overriding issue is studied on the basis of the wheel rise in train collisions. The results show that the second bogie of the first colliding vehicle possesses the maximal wheel rise. The wheel rise increases with the increase of vehicles. However, the number of vehicles has tiny influence on the overriding in train collisions at low speeds. On the contrary, the impact speed has significant influence on the overriding in train collisions. The wheel rise increases rapidly if the impact speed is close to the critical speed of overriding. The large wheel rise is principally generated by the great coupler force related to the rigid impact in the axial direction.     

基于不同风格行驶模型的自动驾驶仿真测试自演绎场景研究

为了验证自动驾驶汽车决策结果的安全性,提出一种具有自主决策和交互能力的行驶模型生成方法,该行驶模型作为背景车被用于构建自演绎仿真场景来测试自动驾驶汽车的连续决策能力。首先,以强化学习为基础、结合遗传与进化思想,创新地设计并生成了具有自主决策和交互能力的不同风格行驶模型;然后,在模型构建阶段分别训练生成了保守、普通和激进3种风格的行驶模型,其中普通风格行驶模型的训练参数来源于自然驾驶数据集highD的车辆参数分布,保证了该行驶模型的真实性;最后,在普通风格行驶模型的基础上设计并训练出了具有显著激进特征的激进风格行驶模型,以增强自演绎场景的复杂性和测试效果。结果表明：在模型真实性方面,以highD数据集中的跟车速度、车头间距、换道时刻下碰撞时间等参数的分布为真值,研究所生成的普通风格行驶模型的参数分布与真值的平均相似程度为88%,相较于基于规则的智能驾驶人模型（IDM）提升了20.3%;在场景测试性方面,以被测系统为主要责任方的碰撞次数为评估指标,研究生成的不同风格行驶模型所构成的自演绎场景的测试性约是由IDM构成的基线场景的7倍。因此,设计和生成的行驶模型所构成的自演绎场景可以有效支撑面向自动驾驶决策系统的仿真测试。     

基于自然驾驶数据的自动驾驶汽车安全性评价方法

自动驾驶汽车进行大规模市场推广前必须进行准确可靠的安全性评价，由于自动驾驶系统复杂程度的增加及设计运行区域的扩大，面向传统汽车的评价方法已不能满足自动驾驶汽车的安全性评价需求,基于此，建立一种基于自然驾驶数据的自动驾驶汽车安全性评价方法，可解决现有方法在逻辑场景层面安全性评价的缺陷。首先，建立基于自然驾驶数据的逻辑场景构建流程，分析场景描述参数，搭建自然驾驶数据采集平台采集相关自然驾驶数据，采用高斯分布模型描述参数概率分布；进而，离散逻辑场景参数空间获取具体测试用例，并在建立的PreScan、CarSim和MATLAB联合仿真平台中对被测自动驾驶算法进行仿真遍历测试，通过高斯模型将测试结果中的危险场景参数聚类，获取被测算法在逻辑场景中的危险区域；最后，综合考虑逻辑场景参数空间概率分布和得到的相应逻辑场景危险区域，提出基于自然驾驶数据的自动驾驶汽车安全性评价指标——场景风险指数，并以前车制动和前车切入场景为例，给出某黑盒算法的具体评价示例。研究结果表明：被测算法在前车制动场景和前车切入场景中的场景风险指数分别为0.409 8和1.08×10^-5，在前车制动场景中具有较大的安全风险，与仿真测试的直观结果相符；通过比较计算得到的场景风险指数与实际仿真测试结果可证明所提出的方法可以实现逻辑场景层面的自动驾驶安全性量化、易于操作、贴近自然驾驶情况。     

基于NAIS数据库中视频信息的人—车碰撞事故特征分析

选取中国国家车辆事故深度调查体系(NAIS)数据库中51例包含视频的人—车碰撞事故,进行了特征分析,分析内容包括:人—车碰撞危险场景、碰撞前人—车相对位置、行人碰撞运动响应、人—车碰撞包络线分布、头部落点分布等。结果表明:提取的10种场景,基本覆盖了各种人—车碰撞事故工况;对行人的探测,视场角(FoV)比探测距离更重要;轿车易导致行人正向旋转,单厢车易导致行人负向旋转;人—车碰撞包络线(WAD)主要集中在车辆两侧;致命伤的头部落点主要集中在前风窗玻璃下半部分、左右侧中部以及A柱附近。因此,基于碰撞视频信息可提高人—车碰撞事故特征分析的准确性。     

道路分隔栏的安全性能评价研究

通过对道路分隔栏的实际应用情况与车辆碰撞事故特征分析,在借鉴国内外相关规范的基础上,从碰撞车型、碰撞质量、碰撞速度、碰撞角度、碰撞点位置等因素出发,结合分隔栏事故特征和车辆乘员安全指标,研究确定了道路分隔栏的碰撞试验参数与安全性能评价标准,并利用该试验参数和评价标准,通过有限元仿真技术手段对目前常见的道路分隔栏结构进行了安全性能评价。研究表明：在车辆碰撞分隔栏的事故中,事故车辆主要为小型客车,且分隔栏杆侵入乘员舱造成人员伤害的案例较多,而其他类型伤害较少,因此分隔栏在实际使用中应主要考察车辆碰撞时分隔栏及其构件因为刺入乘员舱对乘员伤害的可能;在1.5 t小客车以60 km/h速度正碰条件下,某常用道路分隔栏的横向杆件侵入了车辆乘员舱,其安全性能不满足评价标准要求。研究成果为道路分隔栏安全性能评价标准的制定提供了一定借鉴作用。     

Evaluation of a brake assistance system (BAS) using an injury severity prediction model for pedestrians

Through the years, traffic engineers and researchers have developed a variety of countermeasures to enhance pedestrian safety. Pedestrian-vehicle collisions are regarded as the most serious type of accident since they incur high fatality rates. A fundamental concept in developing effective countermeasures is to analyze pedestrian-vehicle collisions scientifically, which can identify the causes of accidents and accident severity. The objective of this study was to investigate the pedestrian safety benefit of the brake assistance system (BAS) and a functional requirement associated with BAS, namely the time needed to safely detect a pedestrian ahead. An injury severity prediction model for pedestrians was developed to systematically evaluate the BAS in this study. Ordered multinomial logistic regression analysis was used to establish a statistical model capable of predicting pedestrian injury severity. In addition to vehicle characteristics, collision speed and pedestrian characteristics were used as independent predictor variables. The outcomes of this study would be useful in directing the development of safety policies and technologies associated with pedestrian safety.     

车辆防追尾碰撞安全系统

高速公路的事故类型中追尾碰撞占了很大的比例,因而开发车辆防追尾碰撞安全系统是非常必要的。文章分析了影响行车安全的各种因素,建立了安全车距数学模型,利用现代技术,构造了一种车辆防追尾碰撞安全系统。指出此安全系统能减少由于驾驶员分心和疲劳等原因导致的交通事故,最大限度地提高车辆行驶的安全性。     

To fully automate or not? Investigating demands and willingness to pay for autonomous vehicles based on automation levels

We review the literature on the motivators of consumers’ purchasing decisions regarding autonomous vehicles (AVs), focusing on environmental awareness, vehicle types, concerns about accidents, and merits. The willingness to buy (WTB) and willingness to pay (WTP) for AVs have been extensively studied. However, it is imperative to broaden the outlook and consolidate the existing knowledge base. Although the WTB and WTP for AVs have been studied, automation-level preferences have been underresearched. Based on more than 150,000 observations, we construct a choice model and calculate the WTP for each level of automation. Our results show that there is a disparity between people's WTB and WTP according to the automation level. In particular, we find that the coefficients regarding FAVs’ benefits and accidents significantly exceed those related to environmental concerns. Such results indicate that practical benefits and concerns influence AV demand and WTP to a greater extent than environmental awareness. Our model results indicate a disparity between WTB and WTP for AVs that mostly derives from the different types of environmental concerns, concerns about accidents, and perceived benefits from these automobiles. Our results urge policies according to which governments and companies closely examine consumers who exhibit WTB-WTP disparities. Issues relating to demand patterns, WTP, and a suitable policy framework are discussed.     

垂直侧碰电动自行车下的小汽车超速辨识方法

为了更加方便准确地判定在垂直侧碰电动自行车事故中小汽车是否超速,以城市交叉口垂直侧碰事故为例,提出了一种垂直侧碰电动自行车下的小汽车超速辨识方法.以碰撞时刻为分界点,将碰撞事故按照时间顺序分为碰撞前和碰撞后2个阶段,采用逆向还原事故发生的方法,结合抛距与碰撞速度的经验公式提出了小汽车碰撞速度模型,在测得制动痕迹长度的基础上利用动能定理构建了小汽车制动时刻速度模型.运用PC-Crash仿真,结果表明,小汽车制动时刻速度在34~38 km/h时,辨识误差为1.47%;在42~70 km/h时,误差小于1.3%.通过比较可知,辨识精度相对GIDAS最多可提升15.94%,可精确地分析小汽车垂直侧碰电动自行车事故,并辨识小汽车有无超速,但仍需提高在34~38 km/h区间内的辨识精度.      

无信号灯十字路口自动驾驶汽车直行关键场景构建

自动驾驶汽车在开发过程中需要进行全面的测试以确保其安全性，关键场景是有条件和高度自动驾驶汽车进行安全测试验证的重要基础。首先，针对测试场景参数离散化全组合后测试用例数量巨大的问题，分别采用3路定强度和变强度组合测试策略及场景初筛规则，以自动驾驶主车直行通过无信号灯十字路口及周围2辆干扰车的测试场景为例，使测试用例数量从全组合的3.9×10⁷组分别减少到6 525组和26 496组。其次，采用碰撞时间、后侵占时间和最大减速度作为场景关键性识别和评价指标。利用基于动态安全区域的避障策略和基于模型预测控制的自动驾驶主车运动规划与控制模型，以及搭建的基于MATLAB/Simulink和CarSim软件的自动驾驶汽车联合仿真平台，通过仿真并与安全指标阈值比较，获得数量大幅减少的关键测试用例。最后，采用基于加权欧氏距离的K-medoids聚类方法对变强度组合策略获得的2 234组关键测试用例进行聚类，获得9组典型关键测试用例。研究结果表明：碰撞时间、后侵占时间和最大减速度指标阈值可以用于识别十字路口场景关键性；相对3路定强度参数组合策略，变强度参数组合策略提供了更多的关键测试用例，对其随机抽样获得少量随机关键测试用例；随机和典型关键测试用例可以应用于封闭试验场验证无信号灯十字路口自动驾驶汽车直行运动规划的安全性。     

Development of probabilistic pedestrian fatality model for characterizing pedestrian-vehicle collisions

Pedestrian-related accidents are considered to be the most serious of traffic accidents due to the associated high fatality rates. In Korea, pedestrian fatalities accounted for approximately 40% of all traffic-related fatalities in 2004. Significant efforts have been made to develop effective countermeasures for pedestrian-vehicle collisions. A basis for devising such countermeasures is to understand the characteristics of pedestrian-vehicle collisions. This study develops a pedestrian fatality model capable of predicting the probability of fatality in pedestrian-vehicle collisions. Binary logistic regression and a probabilistic neural network (PNN) are employed to estimate the probability of pedestrian fatality. Pedestrian age, vehicle type and collision speed are used as independent variables of the fatality model. The models developed herein are valuable tools that can be used to direct safety policies and technologies associated with pedestrian safety.     

考虑驾驶员选择通过行为的机非严重冲突判别

现有的严重冲突判别方法并不适用于我国的机非严重冲突判别,而驾驶员的选择通过行为对交通事故能否发生起到了至关重要的作用,因此该研究考虑了驾驶员的选择通过行为,提出了机非严重冲突的判别方法.研究首先通过坐标转换技术对实验视频进行了预处理,之后对交叉口内机非冲突数据进行提取,应用概率统计的方法,着重考虑驾驶员的因素,提出临界冲突时间差(CTDTC)的概念表示驾驶员的选择通过行为.研究发现当冲突时间差(TDTC)的绝对值小于0.66 s 时,驾驶员的选择通过行为会发生分歧,易引发交通事故,因此将其确定为机非严重冲突的判定条件,并进行了验证实验.验证实验结果表明,以冲突时间差(TDTC)的绝对值为0.66 s 作为机非严重冲突的判别阈值时,观测的非严重冲突的累计频率接近15%,严重冲突的累计频率接近85%,该结果与累计频率法相符.该方法的优势在于提出了明确的严重冲突判别阈值,判别结果不受人的主观因素干扰,因此更加准确客观.      

小汽车与两轮车碰撞运动轨迹模型构建

应用碰撞动力学理论对小汽车与两轮车碰撞过程进行分析,分别构建了小汽车与两轮车碰撞后各自的运动轨迹模型,并运用自编软件实现车辆碰撞运动轨迹的再现仿真;选取碰撞事故案例,将求解结果与PC-Crash软件再现数据对比分析,结果表明模型可信度较高。     

基于中国保险汽车安全指数 C-IASI的商业车险纯保费研究

车险作为我国第一财险险种，费率厘定不够精准一直是行业发展的痛点。在国外发达保险市场上，汽车碰撞安全等级及其修复性一直是重要费率厘定因子，并设有相关机构进行汽车碰撞测试。为了弥补我国在这方面的空白，中保研汽车技术研究院和中国汽车工程研究院在中国保险协会的指导下跨界合作开展低速碰撞测试，模拟事故常见的正碰和追尾，从耐撞性、维修经济性、维修比、维修费用、气囊是否直接起爆等 5个维度进行汽车耐撞性及经济维修性测评，构建中国保险汽车安全指数C-IASI。通过引入C-IASI的9项指标，并建立泊松-伽马模型和Tweedie模型，探究汽车自身耐撞性和维修经济性导致的不同赔付差异与机动车辆保费的适配性。实证结果表明，加入中国保险汽车安全指数C-IASI后，商业车险纯保费预测效果有明显提升，泊松-伽马模型和Tweedie模型预测效果接近。     

缆索护栏安全性能研究

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