美国侧面碰撞规程下轿车变形侵入仿真研究

为更好地分析汽车侧面碰撞过程中驾乘人员安全,根据美国联邦机动车安全法规FMVSS214碰撞要求,采用显式有限元分析软件LS-DYNA详细建立了某公司轿车有限元整车模型和可变形移动壁障有限元模型,研究了美国侧面碰撞规程下可变形移动壁障以54 km/h速度撞击轿车侧面的变形侵入过程仿真模拟和轿车侧面车门不同水平级的变形侵入对乘员损伤程度的影响。结果表明:有限元模拟结果与实车碰撞试验结果吻合较好,模型可信;轿车车门中部位置对驾乘人员损伤影响最大,乘员损伤AIS值接近3(严重损伤);参数加权平均变形侵入量能较好的反映车辆侧面碰撞下车身侧围的变形侵入情况。     

副级吸能机构对客车-轿车正面碰撞影响的分析

为解决客车与轿车在纵梁高度吸能位置正面碰撞不兼容的问题,设计一种客车前部副级吸能机构。利用三维显式有限元分析软件LS-DYNA 3D,建立轿车Taurus和中型客车6900Y面对面100%重叠碰撞下的数值仿真模型。研究副级吸能机构对解决汽车前部吸能高度不兼容问题的影响,对比分析2车正面碰撞过程中轿车方向盘侵入量、侵入速度和客车车门变形量、乘员头部位置加速度。结果表明:未装副级吸能机构时,轿车方向盘侵入速度和Z方向侵入量较大,容易发生钻碰现象;客车前部副级吸能机构能降低轿车方向盘的侵入量和侵入速度,同时能减小客车驾驶室车门变形量,降低乘员头部位置加速度。     

我国加快完善汽车碰撞安全法规

中国汽车技术研究中心碰撞试验研究室主任刘玉光日前透露，为了保护司机和乘客安全，我国正在加快完善汽车安全法规，年内将推出侧面碰撞相关法规。届时不符合碰撞安全法规的乘用车将不得在市场销售。     

汽车后排座椅侧气囊仿真系统参数优化研究

为提升侧面碰撞中后排乘员胸腹部保护效果,在国内某款汽车原有侧面碰撞系统模型基础上,构建后排座椅侧气囊(RSAB)仿真试验模型,通过各项零部件试验与气囊模型对标后导入侧面碰撞系统试验模型;分析RSAB及气帘对后排被试的保护性能,针对胸腹部损伤防护优化侧面气囊相关参数,设计正交试验,运用极差分析法,得出最优参数组合,并对比优化前后结果与实车试验数据,验证优化方案的有效性与可行性。研究结果表明:通过选取关键参数,结合正交试验设计和极差分析法,对胸部气囊的泄气孔直径、位置以及点火时刻进行参数匹配优化,得到座椅侧气囊防护最优参数组合为泄气孔位置在上方、点火时刻为5 ms、泄气孔直径为25 mm,胸腹部压缩量可分别降低21.29%和18.93%。     

汽车一体化安全与行人保护研究

汽车一体化安全把汽车被动安全与主动安全有机结合,可以充分发挥主、被动安全措施的最佳效用,其代表技术是汽车预碰撞安全,该技术已成为汽车安全领域新的研究热点和发展趋势。笔者介绍了汽车一体化安全的定义与组成,通过应用实例概述了汽车预碰撞安全的研究现状。通过统计分析人-车碰撞时序探讨汽车一体化安全技术应用于行人保护的必要性。在此基础上介绍了基于一体化安全的行人碰撞保护方案的基本原理,列举应用实例分析该方案的技术特点,应用实例表明一体化安全可以为行人提供更好的保护效果,将成为行人碰撞保护今后的发展方向。     

汽车行人碰撞接触中行人运动学规律仿真研究

基于交通事故模拟分析PC-Crash软件及其内嵌多体系统动力学分析MADYMO模块,建立并验证了车辆多体模型和行人多体模型;对汽车与行人碰撞接触阶段的行人运动学具有较大影响的因素展开广泛分析,并构建汽车行人碰撞仿真试验方案;通过选取对汽车与行人碰撞接触阶段具有较大影响的因素作为仿真试验的自变量,对不同碰撞环境下汽车与行人碰撞接触过程中的行人运动学规律(包括运动姿态和对应的碰撞车速阈值)进行深入研究;汽车行人碰撞仿真与真实事故以及碰撞试验对比具有较好的规律吻合性和一致性。研究表明,笔者采用的计算机建模仿真方法在汽车行人碰撞运动学研究中具有实用价值。     

基于正交试验的碰撞事故中骑车人损伤因素分析

为研究汽车与摩托车碰撞事故中不同因素对骑车人损伤的影响,基于国家车辆事故深度调查体系(NAIS)中的一个真实案例,运用PC-Crash软件建立汽车前部与摩托车右侧的碰撞模型;在此模型基础上,从汽车、摩托车、摩托车骑车人等3个方面考虑影响因素并设计正交试验,分析其权重大小,并探讨显著性因素对骑车人损伤的影响过程。研究表明:汽车车速、骑车人坐高、发动机罩前端缘离地高度等是显著影响损伤的因素;骑车人坐高越低,骑车人与汽车发生碰撞时间缩短,碰撞后造成骑车人的损伤越重;汽车发动机罩前端缘离地高度越大,碰撞后造成骑车人的损伤越轻。     

基于SAPSO-BP网络模型的海洋平台落物碰撞损伤分析

为了确保海洋平台作业过程中落物坠落对甲板撞击后的结构安全,对坠落立管撞击海洋平台甲板的过程进行了非线性仿真分析。运用ANSYS/LS-DYNA建立有限元模型,对坠落立管不同撞击角度撞击海洋平台甲板进行模拟计算,得到甲板在不同工况下的损伤情况。运用1种自适应性变异的粒子群优化算法SAPSO与BP神经网络结合［1］,对海洋平台进行落物碰撞损伤分析。研究结果表明:落物与垂直方向偏离5°～10°为坠落时最危险的工况。SAPSO-BP提高了BP神经网络的拟合能力,减小了拟合误差,提高了拟合精度,验证了SAPSO-BP网络模型的实用性和可靠性。综合考虑制定适用于海洋平台落物安全的工作程序和平台及设备的防护措施,为海洋平台作业中落物风险评估和海洋平台作业安全保障提供参考。     

交通事故与高速公路护栏的选用

高速公路护栏是一种当车辆与之发生接触时，能够通过自身变形来吸收碰撞能量，通过自身角度来引导车辆改变行驶方向，阻止失控车辆越出路外或进入对向车道的吸能部件。护栏可以减少汽车剧烈碰撞时对乘员造成的伤害，是高速公路以设施的形式保护失控汽车的安全防线。     

电动自行车与汽车侧面碰撞对骑车人的损伤研究

为探究电动自行车与汽车侧面碰撞过程中各因素对骑车人损伤的影响规律,基于国家车辆事故深度调查体系(NAIS)中的事故案例,运用多刚体动力学仿真软件PC-Crash开展重建仿真试验,研究电动自行车与不同汽车车型侧面碰撞的角度和接触位置对电动自行车骑车人损伤的影响。结果表明:电动自行车与汽车侧面碰撞接触位置对骑车人的损伤影响较大,SUV车型,中部碰撞骑车人头部损伤最严重,后部碰撞下肢损伤最严重;而电动自行车与汽车碰撞角度的变化对骑车人损伤的影响没有明显的规律性。     

The Crash Compatibility of Cars and Light Trucks

This paper investigates the compatibility of cars, light trucks, and vans (LTVs) involved in traffic crashes. An analysis of U.S. crash statistics shows that, although LTVs currently account for approximately one–third of registered U.S. passenger vehicles, collisions between cars and LTVs account for over one–half of all fatalities in light vehicle–to–vehicle crashes. In these crashes, 81 percent of the fatally injured are found to be occupants of the car. These statistics suggest that LTVs and passenger cars are incompatible in traffic crashes, and that LTVs are the more aggressive of the two vehicle classes. The fundamental incompatibility between cars and LTVs is observed even when the analysis is restricted to collisions between vehicles of model year 1990 or later - indicating that, despite the availability of newer safety countermeasures, e.g., airbags, the incompatibility between cars and LTVs will persist in future fleets. Through examination of crash test results, field crash statistics, and vehicle measurements, the paper explores the design imbalances between cars and LTVs, e.g., mass, stiffness, and geometry, which lead to these severe crash incompatibilities.     

Can post encroachment time substitute intersection characteristics in crash prediction models?

IntroductionTransportation safety analyses have traditionally relied on crash data. The limitations of these crash data in terms of timeliness and efficiency are well understood and many studies have explored the feasibility of using alternative surrogate measures for evaluation of road safety. Surrogate safety measures have the potential to estimate crash frequency, while requiring reduced data collection efforts relative to crash data based measures. Traditional crash prediction models use factors such as traffic volume, sight distance, and grade to make risk and exposure estimates that are combined with observed crashes, generally using an Empirical Bayes method, to obtain a final crash estimate. Many surrogate measures have the notable advantage of not directly requiring historical crash data from a site to estimate safety. Post Encroachment Time (PET) is one such measure and represents the time difference between a vehicle leaving the area of encroachment and a conflicting vehicle entering the same area. The exact relationship between surrogate measures, such as PET, and crashes in an ongoing research area.MethodThis paper studies the use of PET to estimate crashes between left-turning vehicles and opposing through vehicles for its ability to predict opposing left-turn crashes. By definition, a PET value of 0 implies the occurrence of a crash and the closer the value of PET is to 0, the higher the conflict risk.ResultsThis study shows that a model combining PET and traffic volume characteristic (AADT or conflicting volume) has better predictive power than PET alone. Further, it was found that PET may be capturing the impact of certain other intersection characteristics on safety as inclusion of other intersection characteristics such as sight distance, grade, and other parameters result in only marginal impacts on predictive capacity that do not justify the increased model complexity.     

Investigating the different characteristics of weekday and weekend crashes

IntroductionThis study provides a systematic approach to investigate the different characteristics of weekday and weekend crashes.MethodWeekend crashes were defined as crashes occurring between Friday 9 p.m. and Sunday 9 p.m., while the other crashes were labeled as weekday crashes. In order to reveal the various features for weekday and weekend crashes, multi-level traffic safety analyses have been conducted. For the aggregate analysis, crash frequency models have been developed through Bayesian inference technique; correlation effects of weekday and weekend crash frequencies have been accounted. A multivariate Poisson model and correlated random effects Poisson model were estimated; model goodness-of-fits have been compared through DIC values. In addition to the safety performance functions, a disaggregate crash time propensity model was calibrated with Bayesian logistic regression model. Moreover, in order to account for the cross-section unobserved heterogeneity, random effects Bayesian logistic regression model was employed.ResultsIt was concluded that weekday crashes are more probable to happen during congested sections, while the weekend crashes mostly occur under free flow conditions. Finally, for the purpose of confirming the aforementioned conclusions, real-time crash prediction models have been developed. Random effects Bayesian logistic regression models incorporating the microscopic traffic data were developed. Results of the real-time crash prediction models are consistent with the crash time propensity analysis. Furthermore, results from these models would shed some lights on future geometric improvements and traffic management strategies to improve traffic safety.Impact on IndustryUtilizing safety performance to identify potential geometric improvements to reduce crash occurrence and monitoring real-time crash risks to pro-actively improve traffic safety.     

基于虚拟碰撞试验分析燃料电池轿车车身结构的安全性

在燃料电池轿车的车身结构配合动力驱动系统的改进而不断改进的过程中,需要从碰撞安全性角度去分析结构方案的优劣.本文采用虚拟正面碰撞试验方法,研究经过改进后的燃料电池轿车的半承载式车身是否满足国家被动安全法规,并通过与第1代燃料电池轿车的整车碰撞模拟结果的对比,分析车身结构改进对正面碰撞安全性的影响,从而为进一步的结构改进提供有价值的建议.     

带乘员及约束系统汽车正面碰撞的有限元法仿真研究

采用计算机模拟的方法,对国产某轿车发生正面碰撞时,乘员在佩带三点式安全带的约束状态下的运动响应进行研究,从乘员的运动响应情况、乘员舱的变形情况、假人的HIC值等几个方面分析了该车型的乘员保护安全性能.模拟结果表明,该车达到了安全法规的要求.并探讨了运用有限元法对带乘员及约束系统的整车正面碰撞的计算机模拟方法.     

Crash risk factors for interstate large trucks in North Carolina

Introduction: Provide an updated examination of risk factors for large truck involvements in crashes resulting in injury or death. Methods: A matched case–control study was conducted in North Carolina of large trucks operated by interstate carriers. Cases were defined as trucks involved in crashes resulting in fatal or non-fatal injury, and one control truck was matched on the basis of location, weekday, time of day, and truck type. The matched-pair odds ratio provided an estimate of the effect of various driver, vehicle, or carrier factors. Results: Out-of-service (OOS) brake violations tripled the risk of crashing; any OOS vehicle defect increased crash risk by 362%. Higher historical crash rates (fatal, injury, or all crashes) of the carrier were associated with increased risk of crashing. Operating on a short-haul exemption increased crash risk by 383%. Antilock braking systems reduced crash risk by 65%. All of these results were statistically significant at the 95% confidence level. Other safety technologies also showed estimated benefits, although not statistically significant. Conclusions: With the exception of the finding that short-haul exemption is associated with increased crash risk, results largely bolster what is currently known about large truck crash risk and reinforce current enforcement practices. Results also suggest vehicle safety technologies can be important in lowering crash risk. This means that as safety technology continues to penetrate the fleet, whether from voluntary usage or government mandates, reductions in large truck crashes may be achieved. Practical application: Results imply that increased enforcement and use of crash avoidance technologies can improve the large truck crash problem.     

Modelling the relationship of driver license and offense history with fatal and serious injury (FSI) crash involvement

Introduction: Previous research has indicated that increases in traffic offenses are linked to increased crash involvement rates, making reductions in offending an appropriate measure for evaluating road safety interventions in the short-term. However, the extent to which traffic offending predicts fatal and serious injury (FSI) crash involvement risk is not well established, prompting this new Victorian (Australia) study. Method: A preliminary cluster analysis was performed to describe the offense data and assess FSI crash involvement risk for each cluster. While controlling demographic and licensing variables, the key traffic offenses that predict future FSI crash involvement were then identified. The large sample size allowed the use of machine learning methods such as random forests, gradient boosting, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. This was done for the ‘all driver’ sample and five sometimes overlapping groups of drivers; the young, the elderly, and those with a motorcycle license, a heavy vehicle license endorsement and/or a history of license bans. Results: With the exception of the group of drivers who had a history of bans, offense history significantly improved the accuracy of models predicting future FSI crash involvement using demographic and licensing data, suggesting that traffic offenses may be an important factor to consider when analyzing FSI crash involvement risk and the effects of road safety countermeasures. Conclusions: The results are helpful for identifying driver groups to target with further road safety countermeasures, and for showing that machine learning methods have an important role to play in research of this nature. Practical Application: This research indicates with whom road safety interventions should particularly be applied. Changes to driver demerit policies to better target offenses related to FSI crash involvement and repeat traffic offenders, who are at greater risk of FSI crash involvement, are recommended.     

Market basket analysis of crash data from large jurisdictions and its potential as a decision support tool

Data mining applications are becoming increasingly popular for many applications across a set of very divergent fields. Analysis of crash data is no exception. There are many data mining methodologies that have been applied to crash data in the recent past. However, one particular application conspicuously missing from the traffic safety literature until recently is association analysis or market basket analysis. The methodology is used by retailers all over the world to determine which items are purchased together. In this study, crashes are analyzed as supermarket transactions to detect interdependence among crash characteristics. The results from the analysis include simple rules that indicate which crash characteristics are associated with each other. The application is demonstrated using non-intersection crash data from the state of Florida for the year 2004. In the proposed methodology no variable needs to be assigned as dependent variable. Hence, it is useful in identifying previously unknown patterns in the data obtained from large jurisdictions (such as the State of Florida) as opposed to the data from a single roadway or intersection. Based on the association rules discovered from the analysis, it was concluded that there is a significant correlation between lack of illumination and high severity of crashes. Furthermore, it was found that under rainy conditions straight sections with vertical curves are particularly crash prone. Results are consistent with the understanding of crash characteristics and point to the potential of this methodology for the analysis of crash data collected by the state and federal agencies. The potential of this technique may be realized in the form of a decision support tool for the traffic safety administrators.     

Efficacies of roadway safety improvements across functional subclasses of rural two-lane highways

Introduction

Highway crash occurrence is a leading cause of unnatural deaths, and highway agencies continually seek to identify engineering measures to reduce crashes and to assess the efficacy of such measures. Most past studies on the effectiveness of roadway improvements in terms of crash reduction considered all rural two-lane sections as a single category of roads. However, it may be hypothesized that the differences in the mobility and accessibility characteristics that are reflected in (and due to) the different design standards between different functional subclasses in the rural two-lane highway system can lead to differences in efficacies of safety improvements at these subclasses. This paper investigates the efficacy of roadway improvements, in terms of crash reduction, at the various subclasses of rural two-lane highways.

Methods

An empirical analysis of safety performance at each of the three subclasses of rural two-lane highways was carried out using the negative binomial modeling technique. For each subclass, crash prediction models were developed separately for the three levels of crash severity: property-damage only, injury, and fatal/injury. The crash factors that were considered include lane width, shoulder width, pavement surface friction, pavement condition, and horizontal and vertical alignments. After having developed the safety performance functions, the effectiveness (in terms of the extent of crash reduction, for different levels of crash severity) of highway safety enhancements at each highway subclass were determined using the theoretical concepts established in past literature. These enhancements include widening lanes, widening shoulders, enhancing pavement surface friction, and improving the vertical or horizontal alignment.

Results and Conclusion

The study found that there is empirical evidence to justify the decomposition of the family of rural two-lane roads into its constituent subclasses for purposes of analyzing the effectiveness of safety enhancement projects and thus to avoid underestimation or overestimation of benefits of safety improvements at this class of highways.     

Teenage driver crash incidence and factors influencing crash injury by rurality

BackgroundPrevious research has identified teenage drivers as having an increased risk for motor-vehicle crash injury compared with older drivers, and rural roads as having increased crash severity compared with urban roads. Few studies have examined incidence and characteristics of teen driver-involved crashes on rural and urban roads.MethodsAll crashes involving a driver aged 10 through 18 were identified from the Iowa Department of Transportation crash data from 2002 through 2008. Rates of overall crashes and fatal or severe injury crashes were calculated for urban, suburban, rural, and remote rural areas. The distribution of driver and crash characteristics were compared between rural and urban crashes. Logistic regression was used to identify driver and crash characteristics associated with increased odds of fatal or severe injury among urban and rural crashes.ResultsFor younger teen drivers (age 10 through 15), overall crash rates were higher for more rural areas, although for older teen drivers (age 16 through 18) the overall crash rates were lower for rural areas. Rural teen crashes were nearly five times more likely to lead to a fatal or severe injury crash than urban teen crashes. Rural crashes were more likely to involve single vehicles, be late at night, involve a failure to yield the right-of-way and crossing the center divider.ConclusionsIntervention programs to increase safe teen driving in rural areas need to address specific risk factors associated with rural roadways.Impact on IndustryTeen crashes cause lost work time for teen workers as well as their parents. Industries such as safety, health care, and insurance have a vested interest in enhanced vehicle safety, and these efforts should address risks and injury differentials in urban and rural roadways.