Filtern
Dokumenttyp
Schlagworte
- Europa (3) (entfernen)
Institut
- Abteilung Fahrzeugtechnik (3)
- Sonstige (1)
Die amtliche Straßenverkehrsunfallstatistik kann nur in begrenztem Umfang Informationen zu Unfallentstehung, Unfallablauf sowie zu den zugrunde liegenden Verletzungsmechanismen bereitstellen. Verbleibende Informationslücken lassen sich durch spezielle Erhebungsteams schließen, die Verkehrsunfälle nach wissenschaftlichen Aspekten dokumentieren. Hierzu unterhalten das Bundesministerium für Verkehr, Bau- und Wohnungswesen und die Bundesanstalt für Straßenwesen seit 30 Jahren ein Forschungsprojekt zur Unfalldatenerhebung an der Medizinischen Hochschule Hannover. Seit 1999 erfolgt eine Kooperation mit der Forschungsvereinigung Automobiltechnik (FAT), die ein weiteres Erhebungsteam an der Technischen Universität Dresden unterhält. Die Unfalldaten gehen in die gemeinsame GIDAS-Datenbank ein, aus der sich umfassende Informationen zu den breit gefächerten Forschungsfeldern "Passive und aktive Fahrzeugsicherheit", "Verkehrs- und Rettungsmedizin" und "Straßenbezogene Sicherheitsfragen" gewinnen lassen. In der Zukunft werden Unfallvermeidungsstrategien und Unfallursachenprophylaxe im Vordergrund einer prospektiven Unfallforschung stehen. Die Daten werden auch in Zukunft für die weitere Verbesserung der Verkehrssicherheit einen bedeutenden Beitrag leisten.
The European Enhanced Vehicle-safety Committee wants to promote the use of more biofidelic child dummies and biomechanical based tolerance limits in regulatory and consumer testing. This study has investigated the feasibility and potential impact of Q-dummies and new injury criteria for child restraint system assessment in frontal impact. European accident statistics have been reviewed for all ECE-R44 CRS groups. For frontal impact, injury measures are recommended for the head, neck, chest and abdomen. Priority of body segment protection depends on the ECE-R44 group. The Q-dummy family is able to reflect these injuries, because of its biofidelity performance and measurement capabilities for these body segments. Currently, the Q0, Q1, Q1.5, Q3 and Q6 are available representing children of 0, 1, 1.5, 3 and 6 years old. These Q-dummies cover almost all dummy weight groups as defined in ECE-R44. Q10, representing a 10 year-old child, is under development. New child dummy injury criteria are under discussion in EEVC WG12. Therefore, the ECE-R44 criteria are assessed by comparing the existing P-dummies and new Q-dummies in ECE-R44 frontal impact sled tests. In total 300 tests covering 30 CRSs of almost all existing child seat categories are performed by 11 European organizations. From this benchmark study, it is concluded that the performance of the Q-dummy family is good with respect to repeatability of the measurement signals and the durability of the dummies. Applying ECE-R44 criteria, the first impression is that results for P- and Q-dummy are similar. For child seat evaluation the potential merits of the Q-dummy family lie in the extra measurement possibilities of these dummies and in the more biofidelic response.
In the paper it is investigated to what extend one can extrapolate the detailed accident database GIDAS (German In-Depth Accident Study), with survey area Hanover and Dresden region, to accident behavior in other regions and countries within Europe and how such an extrapolation can be implemented and evaluated. Moreover, it is explored what extent of accident data for the target country is necessary for such an extrapolation and what can be done in situations with sparse and low accident information in a target region. It will be shown that a direct transfer of GIDAS injury outcomes to other regions does not lead to satisfactory results. But based on GIDAS and using statistical decision tree methods, an extrapolation methodology will be presented which allows for an adequate prediction of the distribution of injury severity in severe traffic accidents for European countries. The method consists essentially of a separation of accidents into well-described subgroups of accidents within which the accident severity distribution does not vary much over different regions. In contrast the distribution over the various subgroups of accidents typically is rather different between GIDAS and the target. For the separation into the subgroups meaningful accident parameters (like accident type, traffic environment, type of road etc.) have been selected. The developed methodology is applied to GIDAS data for the years 1999-2012 and is evaluated with police accident data for Sweden (2002 to 2012) and the United Kingdom (2004 to 2010). It is obtained that the extrapolation proposal has good to very good predictive power in the category of severe traffic accidents. Moreover, it is shown that iterative proportional fitting enables the developed extrapolation method to lead to a satisfactory extrapolation of accident outcomes even to target regions with sparse accident information. As an important potential application of the developed methodology the a priori extrapolation of effects of (future) safety systems, the operation of which can only be well assessed on the basis of very detailed GIDAS accident data, is presented. Based on the evaluation of the presented extrapolation method it will be shown that GIDAS very well represents severe accidents, i.e. accidents with at least one severely or fatally injured person involved, for other countries in Europe. The developed extrapolation method reaches its limits in cases for which only very little accident information is available for the target region.