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In general the passive safety capability is much greater in newer versus older cars due to the stiff compartment preventing intrusion in severe collisions. However, the stiffer structure which increases the deceleration can lead to a change in injury patterns. In order to analyse possible injury mechanisms for thoracic and lumbar spine injuries, data from the German Inâ€Depth Accident Study (GIDAS) were used in this study. A twoâ€step approach of statistical and caseâ€byâ€case analysis was applied for this investigation. In total 4,289 collisions were selected involving 8,844 vehicles, 5,765 injured persons and 9,468 coded injuries. Thoracic and lumbar spine injuries such as burst, compression or dislocation fractures as well as soft tissue injuries were found to occur in frontal impacts even without intrusion to the passenger compartment. If a MAIS 2+ injury occurred, in 15% of the cases a thoracic and/or lumbar spine injury is included. Considering AIS 2+ thoracic and lumbar spine, most injuries were fractures and occurred in the lumbar spine area. From the case by case analyses it can be concluded that lumbar spine fractures occur in accidents without the engagement of longitudinals, lateral loading to the occupant and/or very severe accidents with MAIS being much higher than the spine AIS.
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.
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.
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.
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.
For the estimation of the benefit and effect of innovative Driver Assistance Systems (DAS) on the collision positions and by association on the accident severity, together with the economic benefit, it becomes necessary to simulate and evaluate a variety of virtual accidents with different start values (e.g. initial speed). Taken into account the effort necessary for a manual reconstruction, only an automated crash computation can be considered for this task. This paper explains the development of an automated crash computation based on GIDAS. The focus will be on the design of the virtual vehicle models, the method of the crash computation as well as exemplary applications of the automated crash computation. For the first time an automated crash computation of passenger car accidents has been realized. Using the automated crash computation different tasks within the field of vehicle safety can be elaborated. This includes, for example, the calculation of specific accident parameters (such as EES or delta-V) for various accident constellations and the estimation of the economic benefit of DAS using IRFs (Injury Risk Functions).
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.
The evaluation of the expected benefit of active safety systems or even ideas of future systems is challenging because this has to be done prospectively. Beside acceptance, the predicted real-world benefit of active safety systems is one of the most important and interesting measures. Therefore, appropriate methods should be used that meet the requirements concerning representativeness, robustness and accuracy. The paper presents the development of a methodology for the assessment of current and future vehicle safety systems. The variety of systems requires several tools and methods and thus, a common tool box was created. This toolbox consists of different levels, regarding different aspects like data sources, scenarios, representativeness, measures like pre-crash-simulations, automated crash computation, single-case-analyses or driving simulator studies. Finally, the benefit of the system(s) is calculated, e.g. by using injury risk functions; giving the number of avoided/mitigated accidents, the reduction of injured or killed persons or the decrease of economic costs.
India is one of the leading countries reporting highest road accidents & related injuries. TMARG (Tata Motors Accident Research Group) has been recording crashes in association with M/s. Lokamanya Medical Foundation since 2011 with M/s, Amandeep Hospitals since Aug 2013. This study has highlighted some accident types not discussed extensively in literature. Trucks to Truck impacts " Cabin interaction with overhanging loadbody structures and Offset underside impacts for passenger vehicles are seen in significant numbers. The paper discusses these in more detail including severity.
Safety of light goods vehicles - findings from the German joint project of BASt, DEKRA, UDV and VDA
(2011)
Light goods vehicles (LGVs) are an important part of the vehicle fleet, providing a vital component in the European transportation system. On the other hand, LGVs are in the focus of public discussion regarding road safety. In order to analyse the accident situation of LGVs in an objective manner, Federal Highway Research Institute (BASt), VDA, DEKRA and German Insurers Accident Research (UDV) launched a joint project. The aim of this project, which will be finished by mid of 2011, is to identify reasonable measures which will further improve the safety of LGVs. For the first time, these partners jointly together conducted a research project and put together their know-how in accident research. Analyses are based on real-life accident data from the GIDAS database, the Accident Database of UDV (UDB), the DEKRA database and national statistics. The findings deliver answers to questions within the arena of future legislative actions and consumer protection activities. The analyses of databases cover areas of primary and secondary safety of LGVs with a special focus on advanced driver assistance systems (ADAS), driver behaviour as well as partner and occupant protection. Key figures from national statistics are used to highlight hotspots of accidents of LGVs in Germany. Finally, the proposed countermeasures are assessed regarding their potential effectiveness. Amongst others, the results show that the accident situation of LGVs is very similar to that of passenger cars. Noteworthy variations could be found in collisions with pedestrians, at reversing and regarding accident causes. Occupant safety of LGVs is on a higher level compared to cars. Results indicate that seatbelt use is on a significantly lower level compared to cars. This leads to higher-than-average injury risk for unbelted LGV occupants. When it comes to partner protection, there are problems with compatibility at LGVs. For car occupants there is a very high injury risk when colliding with a LGV. It indicates that higher passive safety test standards for LGVs would be counterproductive if they further increase stiffness of LGVs. The analysis of LGV-pedestrian accidents shows that pedestrian kinematic differs significantly from car-pedestrian accidents. At this point, existing pedestrian related test standards developed for cars cannot be adopted to LGVs. When it comes to active safety, ESC proved its effectiveness once again. Beyond that, rear view cameras, advanced emergency braking systems and lane departure warning systems show a safety potential, too. In addition to any technical countermeasures previously discussed, the importance of the driver behavior and attitude regarding the accident risk was investigated. In order to develop successful actions it is important to understand the main target population. In the case of LGV especially the crafts business and smaller companies are the major contributors the safety issue.