6th International Conference on ESAR
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Schlagworte
- Accident reconstruction (14) (entfernen)
Institut
- Sonstige (14)
- Abteilung Fahrzeugtechnik (1)
Analysis of pedestrian leg contacts and distribution of contact points across the vehicle front
(2015)
Determining the risk to pedestrians that are impacted by areas of the front bumper not currently regulated in type-approval testing requires an understanding of the target population and the injury risk posed by the edges of the bumper. National statistics show that approximately 10% of all accident casualties are pedestrians, with 20% to 30% of these pedestrian casualties being killed or seriously injured. However, the contact position across the front of the bumper is not recorded in national statistics and so in-depth accident databases (OTS, UK and GIDAS, Germany) were used to examine injury risk in greater detail. The results showed that some injury types and severities of injuries appear to peak around the bumper edges. Although there are sometimes inconsistencies in the data, generally there is no evidence to suggest that the edges of the bumper are less likely to be contacted or cause injury.
Injury severity of e.g. pedestrians or bikers after crashes with cars that are reversing is almost unknown. However, crash victims of these injuries can frequently be seen in emergency departments and account for a large amount of patients every year. The objective of this study is to analyze injury severity of patients that were crashed into by reversing cars. The Hannover Medical School local accident research unit prospectively documented 43,000 road traffic accidents including 234 crashes involving reversing cars. Injury severity including the abbreviated injury scale (AIS) and the maximum abbreviated injury scale (MAIS) was analyzed as well as the location of the accident. As a result 234 accidents were included into this study. Pedestrians were injured in 141 crashes followed by 70 accidents involving bikers. The mean age of all crash victims was 57 -± 23 years. Most injuries took place on straight stretches (n = 81) as well as parking areas (n = 59), entries (n = 36) or crossroads (n = 24). The AIS of the lower extremities was highest followed by the upper extremities. The AIS of the neck was lowest. The mean MAIS was 1.3 -± 0.6. The paper concludes that the lower extremities show the highest risk to become injured during accidents with reversing cars. However, the risk of severe injuries is likely low.
Pedestrians represent about 20% of the overall fatalities in Europe- road traffic accidents. In this paper a methodology is proposed to understand why the numbers are so high, especially in the south of Europe and particularly in Portugal, . First a detailed statistical analysis using Ordinal Logistic Regression model (OLR) was applied to the gathered data from all Portuguese accidents with victims in the period 2010-2012. In a second stage accident reconstruction computational techniques using pedestrian biomechanical models are used to evaluate the accident conditions that lead to the injuries, such as the speed and the impact location. For biomechanical injury criterions, the AIS (Abbreviated Injury Scale), the HIC (Head Injury Criterion) and other injury criterions based on the resulting accelerations in the pedestrian's body are used. The statistical model reported that there were several predictors that significantly influenced the pedestrian injury severity in the event of a road accident, such as Pedestrian's age, Pedestrian's gender, Vehicle Design/Category or Driver's gender. The use of injury scales and biomechanical criterions in in-depth investigation of road accidents, such as AIS, can significantly improve the quality of the reconstruction process.
Cycling supports the independence and health of the aging population. However, elderly cyclists have an increased injury risk. The majority of injured cyclists is victim of a single-sided accident, an accident in which there is no other party involved. The aim of the project "Safe and Aware on the bicycle" is to develop guidelines for an advisory system that is useful in preventing single-sided accidents. This system is able to support the elderly cyclist; enabling the cyclist to timely adapt his cycling behaviour and improve cycling safety and comfort. For the development of such advisory system the causes of singles accidents and the wishes of the elderly cyclist must be known. First step to obtain this insight was a literature survey and an GIDAS research. Unfortunately accidentology research with GIDAS did not give the full understanding of the pre-crash situations and (especially the behaviour related) factors leading to the accident. The second step was consultation of elderly cyclist through a questionnaire (n=800), in-depth interviews (n=12) and focus group sessions (n=15). This offered complementary information and a much better understanding of the behavioural aspects. Results concern the behaviour in traffic and identify specific physical (i.e. problems looking backwards over the shoulder) and mental issues. Furthermore, the needs and wishes for support in specific cycling situations were identified. In conclusion; The GIDAS results together with the information obtained contacting the elderly cyclists enabled setting up requirements for an advisory system, which is useful in preventing single-sided accidents.
Since a number of human models have been developed it appears sensible to use these models also in the accident analysis. Especially the understanding of injury mechanisms and probably even injury risk curves can be significantly improved when interesting accidents are reconstructed using human body models. However, an important limitation for utilising human models for accident reconstruction is the effort needed to develop detailed FE models of the accident partners or to prepare the human model reconstruction by running physical accident reconstructions. The proposed approach for using human models for accident reconstruction is to use simplified and parametric car models. These models can be adapted to the crash opponents in a fast and cost effective way. Although, accuracy is less compared to detailed FE models, the relevant change in velocity can be simulated well, indicating that the computation of a detailed crash pulse is not needed. Two frontal impact test accidents that were reconstructed experimentally and using the parametric car models are indicating sufficient correlation of the adapted parametric car models with the full scale crash reconstructions. However, further developments of the parametric models to be capable for the use in lateral impacts and rear impacts are needed. For the PC Crash simulation runs the output sampling rate is too large to allow sufficient analysis. In addition the performance appears to be too general.
This study aimed at comparing head Wrap Around Distance (WAD) of Vulnerable Road User (VRU) obtained from the German in-depth Accident Database (GIDAS), the China in-depth Accident Database (CIDAS) and the Japanese in-depth Accident Database (ITARDA micro). Cumulative distribution of WAD of pedestrian and cyclist were obtained for each database (AIS2+) showing that WAD of cyclists were larger than the ones of pedestrians. Comparing three regions, the 50%tile WAD of GIDAS was larger than that of both Asian accident databases. Using linear regression that might predict WAD of pedestrians and cyclists from Impact speed and VRU height, WADs were calculated to be 206cm/219cm (Pedestrian/Cyclist) for GIDAS, 170cm/192cm for CIDAS and 211cm/235cm for ITARDA. In addition, this study may be helpful for reconsideration of WAD measurement alignment between accident reconstruction and test procedures.
The project UR:BAN "Cognitive assistance (KA)" aims at developing future assistance systems providing improved performance in complex city traffic. New state-of-the-art panoramic sensor technologies now allow comprehensive monitoring and evaluation of the vehicle environment. In order to improve protection of vulnerable road users such as pedestrians and cyclists, a particular objective of UR:BAN is the evaluation and prediction of their behaviour and actions. The objective of subproject "WER" is development support by providing quantitative estimates of traffic collisions at the very start and predict potential in terms of optimized accident avoidance and reduction of injury severity. For this purpose an integrated computer simulation toolkit is being devised based on real world accidents (GIDAS as well as video documented accidents), allowing the prediction of potential effectiveness and future benefit of assistance systems in this accident scenario. Subsequently, this toolkit may be used for optimizing the design of implemented assistance systems for improved effectiveness.
This study aimed at developing an injury estimation algorithm for AACN technologies for Germany and compared them to findings based on Japanese data. The data to build and to verify the algorithm was obtained from the German in-depth Accident Database (GIDAS) and split into a training and a validation dataset. Significant input variables and the generalized linear regression model to predict severe injuries (ISS>15) were selected to maximize area under the receiver operating characteristic curve (AUC). Probit regression with the input parameter multiple impact, delta v, seatbelt use and impact direction gave the largest AUC of 0.91. Sensitivity of the algorithm was validated at 90% and specificity at 76% for an injury risk threshold of 2%. It appears that no major differences between Japan and Germany exist for injury estimation based on delta v and impact direction. However, far side impact and multiple crash events appear to be associated with a larger risk increase in the German data.
Road accidents are typically analyzed to address influences of human, vehicle, and environmental (primarily infrastructure) factors. A new methodology, based on a "Venn diagram" analysis, gives a broader perspective on the probable factors, and combinations of factors, contributing both to the occurrence of a crash and to sustaining injuries in that crash. The methodology was applied to 214 accidents on the Mumbai-Pune expressway. Factors contributing to accidents and injuries were addressed. The major human factors influencing accidents on this roadway were speeding (30%) and falling asleep (29%), while injuries were primarily due to lack of seat belt use (46%). The leading infrastructure factor for injuries was impact with a roadside manmade structure (28%), and the main vehicle factor for injuries was passenger compartment intrusion (73%). This methodology can help identify effective vehicle and infrastructure-related solutions for preventing accidents and mitigating injuries in India.
Today's volumes of traffic require more and more responsibility from each individual road user in their interactions. Those who drive motor vehicles have the singular obligation to minimise the risk of accidents and hence the severity of injuries, particularly with a view to the most vulnerable road users such as motor bikes, bikes and pedestrians. Since responsible and pro-active driving depends first and foremost on the visual information relayed by our eyes and the visual channel this requires good command of the traffic and all-round visibility from our driver's seat. Granted that human error can never be fully excluded, improving visibility around the car is nevertheless an urgent priority. To do so, we need to rate visibility in the most realistic driving situations. Since the existing visibility metrics and methodology are not applicable to real-life driving situations, this study aimed at developing a new visibility rating methodology based on real-life accident scenarios. On the basis of the cases documented by the accident research project, this study analysed criteria indicative of diminishing visibility on the one hand and revealing some peculiarities in connection with the visibility issue on the other. Based on the above, the project set out to develop a rating methodology allowing to assess all-round visibility in various road situations taking into account both driver and road geometries. In this context, the assessment of visibility while turning a corner, crossing an intersection and joining traffic on a major road (priority through route) is of major importance. The first tests have shown that critical situations can be avoided by adapting the relevant geometries and technical solutions and that significant improvements of road safety can be derived therefrom.