Abteilung Fahrzeugtechnik
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.
To assess occupant safety in a crash test, criteria associating the measurements made with a crash test dummy to injury risk are necessary. To enable better protection of elderly car occupants the objective of this study was to develop improved thoracic injury criteria for the THOR average male dummy. The development of these criteria is usually based on matched dummy and Post Mortem Human Surrogate (PMHS) tests by relating the obtained PMHS injuries to dummy measurements. This approach is limited, since only a few tests in relevant loading conditions are available and any new test series requires high efforts to be performed due to their complexity and costs. To overcome these limitations and to extend the dataset for the development of THOR dummy chest injury risk functions a simulation-based approach was applied within the EC funded project SENIORS (Safety Enhanced Innovations For older Road Users - www.seniors-project.eu). Within this study frontal impact sled simulations with an FE model representing a THOR average male dummy and matched simulations with a human body model (HBM) representing an elderly car occupant were carried out. The HBM used for this study was the THUMS TUC with modified rib cage, which was developed in SENIORS. The modifications included material and geometry changes aiming to represent an elderly car occupant. The rib fracture risk was predicted with a deterministic approach whereby a rib was considered broken when the strain exceeded an age-dependent threshold. Furthermore, a probabilistic method was applied to predict the probability of sustaining a certain number of fractured ribs by comparing local strain values to the distribution of cortical rib ultimate strain. By relating the output from the HBM simulations to a multi-point dummy injury criterion, injury risk curves were calculated by statistical methods. The wide range of loading conditions resulted in the desired range of injuries and THOR ATD output. The number of fractured ribs predicted by the HBM based on the deterministic prediction method was between 0 and 15. Furthermore, the probabilistic risk for the number of rib fractures equal or greater than two, three or four was calculated for each load case. The THOR rib deflection criterion Rmax was between 18 and 56 mm, while the PC Score was in the range of 2.5 to 7.2. Based on these outputs new risk curves for the predicted deterministic (AIS2+/3+) and probabilistic injury risk were calculated. The new curves show reasonable shapes and significance that provide trust in their application. The new risk curves are compared to risk curves obtained by traditional methods. The results were found similar to previous injury risk functions based on physical tests, which gives a high level of confidence in the chosen approach. The simulation-based approach of matched ATD model vs. HBM simulation was successfully applied. Rmax curves show a slightly better quality than the injury criterion PC Score.