Deriving functional safety (ISO 26262) S-parameters for vulnerable road users from national crash data.

Currently, advanced driver assistance systems (ADAS) and automated vehicles (AV) are designed for use in the existing road infrastructure. These partially and fully automated vehicles will be operated in a shared space not only with other vehicles but also with vulnerable road users (VRU). Even though crashes between ADAS equipped vehicles or AV and VRU seem inevitable in such a scenario, functional safety, i.e., the assessment of the quality and safety level of the automation system, plays a crucial role in minimizing the crash frequency and the injury severity. We develop a data-driven approach to injury severity estimation for functional safety, i.e., ISO 26262 S-parameters, for four types of VRU: pedestrians, bicyclists, scooterists, and motorcycle riders. To estimate the S-parameter, the 90th-percentile of the injury severity distribution in the S-scale, a population-based data set (Germany's national data set DESTATIS) is used. Since the description of the injury severity in DESTATIS is not detailed enough for a direct one-to-one mapping to the S-scale, we enhance the level of detail in the population-based data set by using additional information from the German in-depth accident study (GIDAS), an in-depth, size-limited survey of part of the same population. Thus, we are able to transform the 4-level injury scale (uninjured, slightly injures, severely injured, and fatal) of the police reports found in DESTATIS into the three breakpoints of the injury severity scale (ISS) (ISS ≥{4, 9, 16}) which in turn directly translate to the four levels of the S-scale. Furthermore, the ISS ≥9 breakpoint more or less equates to MAIS 3+, the definition of 'severe injury' in nearly all international road safety goals that look beyond fatalities. The derived injury scale transformation can be utilized to translate the injury severities of the police-reported cases to the politically needed MAIS 3+ distribution. Thus, population-based data can be directly used to estimate the proportion of these 'severely injured.' The crashes are analyzed from the perspective of the VRU as well as from the vehicle type involved. We stratified the opposing vehicles by injury mechanism: wrap projection for bonnet type passenger vehicles (BTV), forward projection for box type vehicles like light trucks (LTV), as well as single-vehicle crashes. We cluster the crash data into traffic domains based on the speed limit: shared zone, residential streets, city roads, arterial thoroughfares, rural roads, and autobahn. For each VRU type, injury mechanism, and traffic domain, the S-parameters, i.e., the 90th-percentile of the injury severity measured in S-scale, are calculated with a one-sided 95% confidence level. Exemplary applications of the results are given in the discussion: an evaluation of an AV hitting a crossing pedestrian, an in-lane swerving ADAS system for VRU avoidance, and the rating of the nominal performance of an inflatable helmet for pedestrians.