Within this paper different European accident data sources were used to investigate the causations and backgrounds of road traffic accidents with pedestrians. Analyses of high level national data and in-depth accident data from Germany and Great Britain was used to confirm and refine preliminary accident scenarios identified from other sources using a literature review. General observations made included that a high proportion of killed or seriously injured pedestrian casualties impacted by cars were in "dark" light conditions. Seven accident scenarios were identified (each divided into "daylight" and "dark" light conditions) which included the majority of the car front-to-pedestrian crash configurations. Test scenarios were developed using the identified accident scenarios and relevant parameters. Hypothetical parameters were derived to describe the performance of pedestrian pre-crash systems based on the assumption that these systems are designed to avoid false positives as a very high priority, i.e. at virtually all costs. As result, three "Base Test Scenarios" were selected to be developed in detail in the AsPeCSS project. However, further Enhanced Test Scenarios may be needed to address environmental factors such as darkness if it is determined that system performance is sensitive to these factors. Finally, weighting factors for the accident scenarios for Europe (EU-27) were developed by averaging and extrapolation of the available data. This paper represents interim results of Work Package 1 within the AsPeCSS project.
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.