Sonstige
Filtern
Dokumenttyp
- Konferenzveröffentlichung (2) (entfernen)
Sprache
- Englisch (2) (entfernen)
Schlagworte
- Accident reconstruction (2)
- Conference (2)
- Deutschland (2)
- Germany (2)
- Japan (2)
- Konferenz (2)
- Unfallrekonstruktion (2)
- Accident (1)
- Berechnung (1)
- Calculation (1)
- Car (1)
- China (1)
- Cyclist (1)
- Estimation (1)
- Fahrzeugteil (Sicherheit) (1)
- Fußgänger (1)
- Head (1)
- Impact (collision) (1)
- Injury (1)
- Kopf (1)
- Length (1)
- Location (1)
- Länge (1)
- Ort (Position) (1)
- Pedestrian (1)
- Pkw (1)
- Post crash (1)
- Prognose (1)
- Radfahrer (1)
- Regression analysis (1)
- Regressionsanalyse (1)
- Schweregrad (Unfall (1)
- Severity (accid (1)
- Unfall (1)
- Unfallfolgemaßnahme (1)
- Vehicle safety device (1)
- Verletzung (1)
- Verletzung) (1)
- Zusammenstoß (1)
- injury) (1)
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.
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.