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Twenty-eight percent of traffic accidents in Japan are rear-end collisions, and of these, 13% are multiple collisions (three or more vehicles and/or roadside objects). A post-crash braking system enables the driver to stop the vehicle in a short distance after a rear-end collision to prevent secondary collisions. In this study, the effectiveness of a post-crash braking system was examined using a drive recorder database. In 64% of rear-end collisions, the driver's braking was interrupted after the collision. The stopping distance was estimated with time data from the drive recorder. We predict that the brake assist would be effective in preventing secondary collisions in 21% of cases.
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.