A framework for rapid on-board deterministic estimation of occupant injury risk in motor vehicle crashes with quantitative uncertainty evaluation

Accurate on-board occupant injury risk prediction of motor vehicle crashes(MVCs)can decrease fatality rates by providing critical information timely and improving injury severity triage rates.The present implemented prediction algorithms in vehicle safety systems are probabilistic and rely on multi-variate logistic regression of real-world vehicle collision databases.As a result,they do not utilize important vehicle and occupant features and tend to overgeneralize the solution space.This study presents a framework to address these problems with deterministic and computationally efficient lumped parameter model simulations driven by a database of vehicle crash tests.A 648-case mixed database was generated with finite element and multi-body models and validated under the principal directions of impact with experimental results for a single vehicle body type.Using the finite element database,we developed lumped parameter models for four principal modes of impact(i.e.,frontal,rear,near side and far side)with parameters identified via genetic algorithm optimization.To obtain confidence bounds for the injury risk prediction,the parameter uncertainty and model adequacy were evaluated with arbitrary and bootstrapped polynomial chaos expansion.The developed algorithm was able to achieve over triage rates of 17.1%±8.5%,whilst keeping the under triage rates below 8%on a finite element-multi body model database of a single vehicle body type.This study demonstrated the feasibility and importance of using low-complexity deterministic models with uncertainty quantification in enhanced occupant injury risk prediction.Further research is required to evaluate the effectiveness of this framework under a wide range of vehicle types.With the flexibility of parameter adjustment and high computational efficiency,the present framework is generic in nature so as to maximize future applicability in improved on-board triage decision making in active safety systems.

Design and Optimization for the Occupant Restraint System of Vehicle Based on a Single Freedom Model

Throughout the vehicle crash event, the interactions between vehicle, occupant, restraint system (VOR) are complicated and highly non-linear. CAE and physical tests are the most widely used in vehicle passive safety development, but they can only be done with the detailed 3D model or physical samples. Often some design errors and imperfections are difficult to correct at that time, and a large amount of time will be needed. A restraint system concept design approach which based on single-degree-of-freedom occupant-vehicle model (SDOF) is proposed in this paper. The interactions between the restraint system parameters and the occupant responses in a crash are studied from the view of mechanics and energy. The discrete input and the iterative algorithm method are applied to the SDOF model to get the occupant responses quickly for arbitrary excitations (impact pulse) by MATLAB. By studying the relationships between the ridedown efficiency, the restraint stiffness, and the occupant response, the design principle of the restraint stiffness aiming to reduce occupant injury level during conceptual design is represented. Higher ridedown efficiency means more occupant energy absorbed by the vehicle, but the research result shows that higher ridedown efficiency does not mean lower occupant injury level. A proper restraint system design principle depends on two aspects. On one hand,the restraint system should lead to as high ridedown efficiency as possible, and at the same time, the restraint system should maximize use of the survival space to reduce the occupant deceleration level. As an example, an optimization of a passenger vehicle restraint system is designed by the concept design method above, and the final results are validated by MADYMO, which is the most widely used software in restraint system design, and the sled test. Consequently, a guideline and method for the occupant restraint system concept design is established in this paper.

The Buffering analysis to identify common geographical factors within the vicinity of severe injury related to motor vehicle crash in Malaysia

BACKGROUND:The main objective was to identify common geographical buildup within the100-meter buffer of severely injured based on injury severity score(ISS) among the motor vehicle crash(MVC) victims in Malaysia.METHODS:This was a prospective cohort study from July 2011 until June 2013 and involved all MVC patients attending emergency departments(ED) of two tertiary centers in a district in Malaysia.A set of digital maps was obtained from the Town Planning Unit of the district Municipal Office(local district map).Vector spaces were spanned over these maps using GIS software(ARCGIS 10.1licensed to the study center),and data from the identified severe injured cases based on ISS of 16 or more were added.Buffer analysis was performed and included all events occurring within a 100-meter perimeter around a reference point.RESULTS:A total of 439 cases were recruited over the ten-month data collection period.Fifty two(11%) of the cases were categorized as severe cased based on ISS scoring of 16 and more.Further buffer analysis looking at the buildup areas within the vicinity of the severely injured locations showed that most of the severe injuries occurred at locations on municipal roads(15,29%),straight roads(16,30%) and within villages buildup(suburban) areas(18,35%).CONCLUSION:This study has successfully achieved its objective in identifying common geographical factors and buildup areas within the vicinity of severely injured road traffic cases.

Key factors contributing to crash severity at highway-rail grade crossings

The purpose of this paper is to develop and com- pare the preferred multinomial logit （MNL） and ordered logit （ORL） model in identifying factors that are important in making an injury severity difference and exploring the impact of such explanatory variables on three different severity levels of vehicle-related crashes at highway-rail grade crossings （HRGCs） in the United States. Vehicle-rail crash data on USDOT highway-rail crossing inventory and public crossing sites from 2005 to 2012 are used in this study. Preferred MNL and ORL models are developed and marginal effects are also calculated and compared. A majority of the variables have shown similar effects on the probability of the three different severity levels in both models. In addition, based on the Akaike information criterion, it is found that the MNL model is better than the ORL model in predicting the vehicle crash severity levels on HRGCs in this study. Therefore, the researchers recommend the use of MNL model in predicting severity levels of vehicle-rail crashes on HRGCs.

Attitudes towards child restrains and seat belts usage in the learned population of Karachi,Pakistan

BACKGROUND:Motor vehicles crashes(MVCs) are the leading cause of injury related morbidity and mortality in developed countries. Recent evidence proves that properly used child seat belts can dramatically reduce the risk of severe and life-threatening injury from MVCs. There are rarities of thought and inspiration regarding the use of child seat belts in our society and region,therefore we lack of data regarding factors and paucity of usage of child seat belts in motor vehicles. This study aimed to assess the knowledge and attitudes of child seat belt usage among the educated population in Karachi,Pakistan.METHODS:Altogether 304 employees were investigated. They were employees of Aga Khan University who were using their cars and having children younger than 10 years old. A cross sectional observational study was designed,and a 36-item questionnaire in English was used to collect data on participants' demographic details,designation,educational level,economic status,validity of driving license,number of children and cars,availability of adult seat belts and child seat belts along with their functionality,awareness,knowledge and attitude toward its use,and reason of not using these devices. SPSS version 20 for Windows was used to analyze the data and the Chi-square test was used.RESULTS:Totally 290 participants were recruited with a response rate of 72%(212). Of 212 participants,126(59%) were male. 154(72.6%) participants had valid driver licenses,and 154(72.6%) had adult seat belts in their vehicles. Only 32(15%) reported regular use of adult seat belts. Although 168(79.2%) participants had some knowledge about child restrains(CRs),only 65(22%) had CRs in their cars. Eighty-two(38.7%) participants got the knowledge about CRs and seat belts from media. Mothers were more concerned about the use of CRs than fathers. Only 14(6.6%) parents were found to use both adult and child seat belts all the time. Of the 157 parents who did not us use CRs,42 considered unnecessary,35 lacked relevant knowledge. But 15 parents used CR against their children's wills.CONCLUSIONS:The pattern of CR usage among the employees at Aga Khan University,Karachi is dictated by the unavailability of CR,followed by ignorance,inconvenience,and nonacceptance by their children. The important issue of CR has consistently been ignored over the years and it has never gained enough popularity in Pakistan.

Prediction of instantaneous driving safety in emergency scenarios based on connected vehicle basic safety messages

Purpose–Basic safety message(BSM)is a core subset of standard protocols for connected vehicle system to transmit related safety information via vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I).Although some safety prototypes of connected vehicle have been proposed with effective strategies,few of them are fully evaluated in terms of the significance of BSM messages on performance of safety applications when in emergency.Design/methodology/approach–To address this problem,a data fusion method is proposed to capture the vehicle crash risk by extracting critical information from raw BSMs data,such as driver volition,vehicle speed,hard accelerations and braking.Thereafter,a classification model based on information-entropy and variable precision rough set(VPRS)is used for assessing the instantaneous driving safety by fusing the BSMs data fromfield test,and predicting the vehicle crash risk level with the driver emergency maneuvers in the next short term.Findings–Thefindings and implications are discussed for developing an improved warning and driving assistant system by using BSMs messages.Originality/value–Thefindings of this study are relevant to incorporation of alerts,warnings and control assists in V2V applications of connected vehicles.Such applications can help drivers identify situations where surrounding drivers are volatile,and they may avoid dangers by taking defensive actions.

Identification of a Driver from the Analysis of Specific Injuries

Determining who was driving a vehicle is often an important part of the assessment of a crash because of the potentially significant medicolegal ramifications.A case is reported of a vehicle containing two occupants.The male occupant had a blood alcohol reading of 85 mg/100 ml and the female occupant(who claimed to be the driver)of 0 mg/100 ml.However,the injuries to the male were more consistent with an impact against the steering wheel and from the posterior displacement of the floor toward the driver’s seat.The injuries sustained by the female occupant were less severe and fewer in number.Blood stains on the edge of the driver’s window and the steering wheel were from the male occupant.It was concluded,therefore,that the male occupant had been the driver and not the female occupant as was being claimed.This result had significant implications in terms of insurance claims and potential criminal charges.