对象驱动的Linux内核crash分类技术研究

Crash(程序崩溃)分析是漏洞挖掘与利用的关键阶段,精准的crash分类是crash分析和漏洞利用的前提.针对现有的Linux内核crash存在大量重复的问题,本文提出一种对象驱动的Linux内核crash分类方法.该方法将内核crash与内核对象的关系建模为二部图结构,从而将crash分类问题转化为内核对象的相似性对比问题.首先,通过对crash执行后向污点分析提取crash相关的内核对象;其次,构造内核对象调用图计算内核与根本原因的相关性度量;最后,基于上述结果构造二部图实现crash相似性比较算法.基于上述方法,本文开发出了Linux内核crash分类的原型系统.通过在真实的数据集上进行实验,验证了系统的有效性和可用性,弥补了现有分类方法粒度较粗,存在误报较多的问题.

基于PC-Crash软件的多车碰撞事故再现研究

近三年来,全国交通事故的年均发生数量在20万次以上,多车碰撞事故的数量也逐年攀升。文章根据车辆碰撞时间的先后顺序将一则复杂的交通碰撞事故,等效成多个简化的碰撞事故,且每个碰撞过程只包含两个事故参与方。通过对事故现场中驾驶员进行损伤验证,以及事故车辆进行痕迹勘验,分析人体损伤程度、各个车辆的碰撞形式和碰撞前的运动速度,运用PC-Crash软件进行事故仿真并验证其真实性。结果表明,仿真得到的人体损伤情况与实际情况相符;事故参与方的最终停止位置、碰撞速度与实际勘验数据相比,误差均小于10%,说明仿真结果与实际勘验的结果相吻合。此方法对复杂的交通碰撞事故能够进行模拟,并且仿真具有一定的真实性,为交通管理部门进行事故责任认定提供了理论支撑。

Injuries Associated with Auto-Tricycle Crashes in an African City: Incidence and Pattern

Purpose: The aim of this study was to determine the incidence and pattern of injuries resulting from auto-tricycle crashes among patients in a tertiary referral centre in Ghana. Methods: Data were retrospectively extracted from hospital records of patients who got involved in auto-tricycle crashes and presented to the Accident and Emergency Centre of the Komfo Anokye Teaching Hospital (KATH), over a one-year period using a structured questionnaire. The gathered data were then entered into an electronic database and then analysed with SPSS version 20.0. Results: The incidence of injury following auto-tricycle crashes over the one-year period was 5.9% (95% CI: 4.9% - 7.0%) with a case fatality rate (FR) of 3.8% (95% CI: 1.3% - 8.7%). All the mortalities resulted from head and neck injuries and none of the patients involved wore a crash helmet. Only 5% of those studied wore crash helmets and were all drivers. Closed fractures accounted for 58% of the injuries, followed by open fractures, 28%. The most commonly fractured bones were the tibia/fibula, followed by the femur and then radius/ulna. The most common mechanism of injury was auto-tricycle toppling over (29%). Passengers were the most injured (48%), followed by drivers (37%) and pedestrians (15%). Most (72%) injuries among participants involved a single body part. On the injury severity scale, most (61%) of patients had minor trauma and 38% had major trauma. Conclusion: Auto-tricycle crashes account for 5.9% of injuries at the study site with a case fatality rate of 3.8%. Passengers had a higher injury rate (48%) than drivers (37%). Fractures of the tibia/fibula were most commonly associated with auto-tricycle crashes. Injuries to the head and neck were responsible for the deaths in the study participants and non-use of a crash helmet was associated with mortalities.

An In-Depth Analysis of Road Fatal Crash Patterns and Discussions in Ho Chi Minh City

Although there has been a slight decrease in road traffic crashes, fatalities, and injuries in recent years, HCMC (Ho Chi Minh City) will continue to encounter challenges in mitigating and preventing road crashes. This study analyzes road crash data from the past five years, obtained from the Road-Railway Police Bureau (PC08) and TSB (Traffic Safety Board) in HCMC. This analysis gives us valuable insights into road crash patterns, characteristics, and underlying causes. This comprehensive understanding serves as a scientific foundation for developing cohesive strategies and implementing targeted solutions to address road traffic safety issues more effectively in the future.

IMPACT和CRASH模型对创伤性颅脑损伤患者预后评估价值的比较研究

背景国际颅脑损伤预后临床测试研究(IMPACT)和重型颅脑损伤后皮质类固醇的随机化研究(CRASH模型是国际上具有影响力的创伤性颅脑损伤(TBI)预后预测模型,需要继续开发,完善和持续的外部验证,以确保对不同环境的普适性。目的同时在中国TBI人群中进行验证IMPACT和CRASH模型的预后评估价值并进行比较。方法选取2017—2019年在郑州大学附属郑州中心医院内接受治疗的TBI患者210例为研究对象,收集纳入患者的基本信息。随访观察患者14 d存活情况和6个月格拉斯哥预后评分(GOS),随访截止时间为2021年6月,终止事件为中途失访。绘制受试者工作特征曲线(ROC曲线)评估IMPACT和CRASH模型对TBI患者预后的预测效能,计算ROC曲线下面积(AUC)。采用Brier评分评价模型的校准度。结果患者平均年龄(54.0±17.4)岁,分别绘制IMPACT模型与CRASH模型预测TBI患者预后的ROC曲线,结果显示IMPACT核心模型、CT模型、实验室模型预测TBI患者6个月GOS预后不良的AUC分别为0.807(95%CI=0.747~0.866,P<0.001)、0.843(95%CI=0.789~0.897,P<0.001)、0.845(95%CI=0.793~0.897,P<0.001),Brier评分分别为0.179、0.164、0.161;IMPACT核心模型、CT模型、实验室模型预测TBI患者6个月死亡的AUC分别为0.868(95%CI=0.816~0.919,P<0.001)、0.896(95%CI=0.851~0.941,P<0.001)、0.892(95%CI=0.850~0.935,P<0.001),Brier评分分别为0.151、0.144、0.136。CRASH基本模型、CT模型预测TBI患者6个月GOS预后不良的AUC分别为0.747(95%CI=0.682~0.813,P<0.001)、0.766(95%CI=0.703~0.829,P<0.001),Brier评分分别为0.306、0.308;CRASH基本模型、CT模型预测TBI患者14 d死亡的AUC分别为0.791(95%CI=0.723~0.860,P<0.001)、0.797(95%CI=0.728~0.865,P<0.001);Brier评分分别为0.348、0.383。结论对于TBI患者的预后,IMPACT模型整体较CRASH模型显示出较好的预测能力。

Understanding the potential key risk factors associated with teen driver crashes in the United States: a literature review

This article is a compilation of teen driver crash contributing factors typically extractable from the crash data collection system in the United States.Tremendous research effort has been undertaken over the decades to comprehend teen driver crash risks,as teen drivers continue to be over-involved in crashes even when accounting for the driving exposure.This article presents the contexts of crash factors related to operating conditions,roadway,vehicle,and driver and their unique influences on teen driver crashes in terms of estimated risk,prevalence,and estimated likelihood mainly from descriptive and analytical studies.The key variables are selected based on the number of studies that considered each risk factor for analysis.The understanding of crash factors could be translated into graduated driver licensing and other teen driver safety programs.While the discussions were grounded in crash studies carried out in the United States,the insights gleaned from these studies hold the potential to offer valuable guidance to other countries.For example,the insights and discussions can serve as a catalyst for the development and improvement of driver education programs tailored to address the specific requirements and difficulties confronted by their teenage drivers.

基于PC-Crash的载人两轮车事故再现仿真

为开发车-载人两轮车事故再现仿真案例,提出事故现场重建、事故参与者建模、仿真再现及结果分析的事故再现流程。通过1例车-载人两轮车事故演示与验证所提流程,发现通过该流程既能解释路面痕迹,又能解释车体变形及人体损伤痕迹,通过简单且可控的方式确保仿真痕迹与事故痕迹最大限度的一致,保证再现结果的客观、可靠。该研究对相关人员开展车-载人两轮车事故的再现及仿真分析工作具有借鉴作用。

Carbon emission trading system and stock price crash risk of heavily polluting listed companies in China:based on analyst coverage mechanism

This study reveals the inconsistencies between the negative externalities of carbon emissions and the recognition condition of accounting statements.Hence,the study identifies that heavily polluting enterprises in China have severe off-balance sheet carbon reduction risks before implementing the carbon emission trading system(CETS).Through the staggered difference-in-difference(DID)model and the propen-sity score matching-DID model,the impact of CETS on reducing the risk of stock price crashes is examined using data from China’s A-share heavily polluting listed companies from 2007 to 2019.The results of this study are as follows:(1)CETS can significantly reduce the risk of stock price crashes for heavily polluting companies in the pilot areas.Specifically,CETS reduces the skewness(negative conditional skewness)and down-to-up volatility of the firm-specific weekly returns by 8.7%and 7.6%,respectively.(2)Heterogeneity analysis further shows that the impacts of CETS on the risk of stock price crashes are more significant for heavily polluting enterprises with the bear market condition,short-sighted management,and intensive air pollution.(3)Mechanism tests show that CETS can reduce analysts’coverage of heavy polluters,reducing the risk of stock price crashes.This study reveals the role of CETS from the stock price crash risk perspective and helps to clarify the relationship between climatic risk and corporate financial risk.

Application of new generation low-strength hot stamping steel to improve the crash performance of BIW

With the improvement of safety performance,car parts have different requirements for material strength and energy absorption performance.The conventional 1500-MPa hot stamping steel cannot well meet the requirements.Considering the new generation 600-MPa hot stamping steel,this study investigates the applicable car parts and hot stamping process,then designs a new body-in-white(BIW)crash test for obtaining the crash performance of the new material.Through the actual part development and crash test,it is verified that the application of the new generation hot stamping steel can improve the crash performance of BIW.

Correlating Connected Vehicle Estimated Deceleration Events with Crash Incidents near Interstate Interchanges

Historical roadway safety analyses have used labor and time-intensive crash data collection procedures. However, crash reporting is often delayed and crash locations are reported with varying levels of spatial accuracy and detail. Recent advances in connected vehicle (CV) data provide an opportunity for stakeholders to proactively identify areas of safety concerns in near-real time with high spatial precision. Public and private sector stakeholders including automotive original equipment manufacturers (OEM) and insurance providers may independently define acceleration thresholds for reporting unsafe driver behavior. Although some OEMs have provided fixed threshold hard-braking event data for a number of years, this varies by OEM and there is no published literature on the best thresholds to use for identifying emerging safety issues. This research proposes a methodology to estimate deceleration events from raw CV trajectory data at varying thresholds that can be scaled to any CV. The estimated deceleration events and crash incident records around 629 interstate exits in Indiana were analyzed for a three-month period from March 1-May 31, 2023. Nearly 20 million estimated deceleration events and 4800 crash records were spatially joined to a 2-mile search radius around each exit ramp. Results showed that deceleration events between -0.5 g and -0.4 g had the highest correlation with an R2 of 0.69. This study also identifies the top 20 interstate exit locations with highest deceleration events. The framework presented in this study enables agencies and transportation professionals to perform safety evaluations on raw trajectory data without the need to integrate external data sources.

Traffic Flow Characteristics in Work Zone and Non-Work Zone Environment and Its Impact on Road Crashes at the Segment Level

This study investigates relationships between congestion and travel time performance metrics and crashes on road segments. The study focuses on work zone routes in Iowa, utilizing 2021 commercially-available probe vehicle data and crash data. Travel time performance metrics were derived from the probe vehicle data, and crash counts were obtained from the crash data. Additional variables included road characteristics (traffic volume, road type, segment length) and a categorical variable for the presence of a work zone. A mixed effect linear regression model was employed to identify relationships between road segment crash counts and the selected performance metrics. This was accomplished for two sets of models that include congestion performance measures at different defining threshold values, along with travel time performance measures. The study results indicate that the congestion indicators, certain travel time performance measures, and traffic counts were statistically significant and positively correlated with crash counts. Indicator variables for rural interstate locations and non-active work zones have a stronger influence on crash count than those for municipal interstate locations and active work zones. These findings can inform decision-makers on work zone safety strategies and crash mitigation planning, especially in high traffic volume areas prone to congestion and queues.

Application of LiDAR Data for Deep Learning Based Near Crash Prediction at Signalized Intersection

Near crash events are often regarded as an excellent surrogate measure for traffic safety research because they include abrupt changes in vehicle kinematics that can lead to deadly accident scenarios. In this paper, we introduced machine learning and deep learning algorithms for predicting near crash events using LiDAR data at a signalized intersection. To predict a near crash occurrence, we used essential vehicle kinematic variables such as lateral and longitudinal velocity, yaw, tracking status of LiDAR, etc. A deep learning hybrid model Convolutional Gated Recurrent Neural Network (CNN + GRU) was introduced, and comparative performances were evaluated with multiple machine learning classification models such as Logistic Regression, K Nearest Neighbor, Decision Tree, Random Forest, Adaptive Boost, and deep learning models like Long Short-Term Memory (LSTM). As vehicle kinematics changes occur after sudden brake, we considered average deceleration and kinematic energy drop as thresholds to identify near crashes after vehicle braking time . We looked at the next 3 seconds of this braking time as our prediction horizon. All models work best in the next 1-second prediction horizon to braking time. The results also reveal that our hybrid model gathers the greatest near crash information while working flawlessly. In comparison to existing models for near crash prediction, our hybrid Convolutional Gated Recurrent Neural Network model has 100% recall, 100% precision, and 100% F1-score: accurately capturing all near crashes. This prediction performance outperforms previous baseline models in forecasting near crash events and provides opportunities for improving traffic safety via Intelligent Transportation Systems (ITS).

Estimating the severity levels of road traffic crashes in Bahrain with crash costs estimated with different approaches

An important issue in analyzing accident blackspots is the estimation of severity levels of different types of accidents.This study aims to estimate the severity level of accidents in Bahrain using crash costs.These crash costs were calculated by the Human Capital Approach(HCA)and total reported costs from the victims.The data was collected from the General Directorate of Traffic,insurance companies,Ministry of Works(MoW)and Ministry of Health.It was found,from the survey responses,that there was no significant effect of victim characteristics on the total cost of the accidents.The severity levels were found to be higher than those found in previous literature or adopted by local authorities which could be attributed to the economic conditions of Bahrain.Moreover,the weights found by both approaches were different from each other.Therefore,it is recommended to use the HCA approach due to its comprehensive calculations involving future costs.

基于PC-Crash的车辆行人事故深入研究

本文依托于中国交通事故深入研究项目(CIDAS),选取CIDAS数据库中的2起典型车辆碰撞行人案例进行事故重建研究。运用多刚体动力学法建立事故车辆的仿真模型,利用PC-Crash深入分析车辆与行人的碰撞过程,得出碰撞车速,研究碰撞后行人的运动响应和各损伤部位的动力学响应,最后将仿真结果和人体损伤的医学鉴定结果进行对比,验证了利用PC-Crash仿真方法重建车辆碰撞行人事故的准确性。

基于PAM-CRASH的鸟撞飞机风挡动响应分析

结合显式动力分析有限元软件PAM-CRASH及其提供的SPH算法,建立了鸟撞飞机风挡数值分析模型。对某飞机风挡进行了动响应分析,计算了风挡中轴线上四点位移时间曲线;鸟撞击过程的仿真结果表明,SPH鸟体模型能有效模拟撞击时鸟体溅射成碎片的情形;建立了鸟撞击作用下风挡破坏判据,对风挡在试验条件下是否破坏进行了模拟计算。计算结果和试验结果吻合较好,表明本文建立的鸟撞飞机风挡数值模型是有效的。

基于Pc-crash的交通事故再现误差分析

对交通事故再现软件Pc-crash中的特征参数进行分析,设计具普遍意义的车-车斜撞模型,运用分析法、控制变量法先提取较主要的参数;再设定正交试验将这些较重要参数按权重大小排序.基于对特征参数的研究,探求这些重要参数对模拟结果影响的权重及产生影响的类型,以此寻求减小事故再现误差的方法,并用国内一起典型的车-两轮车交通事故验证结论的正确性,为提高Pc-crasch软件再现国内事故精度的研究建立基础.

基于Pc-Crash的车-人事故再现

提出基于Pc-Crash的车-人事故再现方法,即以Pc-Crash为仿真平台,构建事故现场及车、人多刚体模型,以车、人接触部位及人、车最终停止位置等事故信息为重要证据对事故进行仿真,并利用人体损伤等其他事故信息验证仿真结果的合理性,最后利用不确定性分析理论评估所得结果的不确定度。以一车-人碰撞事故为例,演示了该方法的步骤。案例再现分析中,事故信息都得到合理利用,并借助响应曲面法与蒙特卡罗方法获得了事故再现结果的分布。算例表明,利用该方法所得再现结果不仅更为客观,且能为事故鉴定提供更多有效信息。

Application of multinomial and ordinal logistic regressionto model injury severity of truck crashes, using violationand crash data

In 2016 alone, around 4000 people died in crashes involving trucks in the USA, with 21% of these fatalities involving only single-unit trucks. Much research has identified the underlying factors for truck crashes.However, few studies detected the factors unique to single and multiple crashes, and none have examined these underlying factors to severe truck crashes in conjunction with violation data. The current research assessed all of these factors using two approaches to improve truck safety.The first approach used ordinal logistic regression to investigate the contributory factors that increased the odds of severe single-truck and multiple-vehicle crashes, with involvement of at least one truck. The literature has indicated that past violations can be used to predict future violations and crashes. Therefore, the second approach used risky violations, related to truck crashes, to identify the contributory factors to the risky violations and truck crashes. Driver actions of failure to keep proper lane following too close and driving too fast for conditions accounted for about 40% of all the truck crashes. Therefore, the same violations as the aforementioned driver actions were included in the analysis. Based on ordinal logistic regression, the analysis for the first approach indicated that being under non-normal conditions at the time of crash, driving on dry-road condition and having a distraction in the cabin are some of the factors that increase the odds of severe single-truck crashes. On the other hand,speed compliance, alcohol involvement, and posted speed limits are some of the variables that impacted the severity of multiple-vehicle, truck-involved crashes. With the second approach, the violations related to risky driver actions,which were underlying causes of severe truck crashes, were identified and analysis was run to identify the groups at increased risk of truck-involved crashes. The results of violations indicated that being nonresident, driving offpeak hours, and driving on weekends could increase the risk of truck-involved crashes. This paper offers an insight into the capability of using violation data, in addition to crash data, in identification of possible countermeasures to reduce crash frequency.

PAM-CRASH碰撞模拟中主要控制参数影响的分析

在汽车耐撞性研究中有限元模拟计算已成为主要的研究手段。本文基于非线性显式有限元软件PAM -CRASH对某车车架前部碰撞过程的模拟计算 ,分析了有限元模型的主要控制参数对计算结果和效率的影响 ,其中包括网格尺寸、沿单元厚度的积分点个数、单元类型和沙漏 (Hourglass)控制。比较了采用不同控制参数对计算结果的载荷 -时间曲线、时间和沙漏能量等的影响。

Modeling traffic barriers crash severity by considering the effect of traffic barrier dimensions

Traffic barriers are in widespread all around the USA as safety countermeasures for reducing the severity of run-off-road crashes. The effect of traffic barriers’ dimension had been ignored in past real-world crash studies due to the considerable cost and time needed for collecting field data. This paper presented two new analytical models to investigate the effect of different variables on the severity of crashes involving traffic barriers, and end treatments. For this reason, a field survey was conducted on over 1.3 million linear feet of traffic barriers (approximately 4,176 miles road) in Wyoming to measure traffic barriers’ geometric features like height, length, offset, and slope rate. The collected data included 55% of all non-interstate roads of Wyoming. Based on results, the crashes involving box beam barriers were less severe than the crashes involved with W-beam or concrete barriers. The traffic barriers with a height between 28 and 31 in. were found safer than the traffic barriers shorter than 28 in., while there was no significant difference between the traffic barriers taller than 31 in. to those shorter than 28 in. in terms of crash severity. The end treatments located nearer to the traffic lane had lower crash severity.