Sonstige
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 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.
The number of injured car occupants decreases constantly. Nevertheless, they account for nearly 50% of all fatalities and about 44% of all seriously injured persons in German traffic accidents. Further reductions of casualties require multiple efforts in all parts of traffic safety. In this paper a detailed analysis of the important pre-hospital rescue phase was done. The basis for future improvements is the knowledge about injury causation of car occupants in combination with other corresponding influence factors. For that reason more than 1.200 severe (AIS3+) injuries of frontal car occupants were analyzed. For the most relevant injuries of car occupants multivariate analysis models were created to predict the probability of these injuries in a real crash scenario. In addition to the collision severity different influence factors like impact direction, seat belt usage, age of the occupant, and gender were analyzed. Furthermore, the models were checked regarding the goodness of fit and all results all results were checked concerning their robustness. The prediction models were created on the basis of 5.000 car accidents. Afterwards, the models were validated using 4.000 different car accidents. The prediction of the probability of severe injuries could be used for different applications in the field of traffic safety. One possibility is the implementation of the models in a tool for the on-the-spot diagnosis. The background for the development of such applications is the fact, that there are only limited diagnostic possibilities available at the accident scene. Nevertheless, the rescue forces have to make essential decisions like the alerting of the necessary medical experts, appropriate treatment, the type of transportation and the choice of an adequate hospital. These decisions quite often decide between life and death or influence the long-term effects of injured persons. At this point, indications of expectable injuries could help enormously. To enable even persons with limited technical knowledge to use the tool, a procedure was developed that facilitates the assumption of the given crash severity. Another important possibility for the application of the prediction models is the use for the qualification of information sent by e-call systems.
Aim of the study was to evaluate the protective effect of bicycle helmets particularly considering injuries to the head and to the face. Accidents with the participation of bicyclists which occurred from 2000 to 2007 were chosen from GIDAS. We observed that injuries to the head and face were more severe in the group of non-helmeted riders. There seems to be no significant difference in injuries with AIS 3-6. Altogether 26 cyclists were killed. 2 of them wore a helmet (1% of helmeted cyclists), 24 did not (1% of non-helmeted cyclists). Only one killed rider (without helmet) did not suffer from polytrauma (only head injuries recorded). The findings seem to support the thesis of a preventive effect of the bicycle helmet, however the two groups are different in their characteristics related to riding speed. Necessarily we need a multivariate model to evaluate the effect of helmets.
In Germany averagely two million traffic accidents happen each year and emergency medical services are called to more than 400 000 patients. Even though this number is decreasing continuously (due to improvements in the fields of vehicle safety, road construction, and accident prevention) every case is yet a challenge for the rescuers and requires improvements in emergency medicine as well. Especially during diagnostics right at the accident scene, there are only limited instruments available to gain the necessary knowledge of the injuries suffered, to come to essential decisions about treatment or transport. To provide an additional diagnostic aid by scouting and estimating the situation, a software-tool calculating the likeliness of the most frequent severe injuries (AIS 3-6) of front occupants in passenger cars has been developed to deliver this necessary information about particular accident scenarios. To achieve this, logistic likelihood functions have been calculated in a multivariate regression analysis analysing all AIS 3+ injuries in the GIDAS database of the years 1999-2006 that happened more than four times