Injury Severity for Hazard & Risk Analyses:Calculation of ISO 26262 S-parameter Values from Real-World Crash Data.

Our goal was the development of a robust and data-driven approach to ISO 26262 injury severity (S-parameter) estimation, replacing the current heuristic methods. The situations investigated as part of an ISO 26262 hazard & risk analysis are broken down into crash configurations. These crashes are analyzed from the perspective of the ADAS-equipped vehicle with the failing system as well as from the crash opponent's point of view. Mainly due to sample size limitations, we focus on belted front-row vehicle occupants. We cluster the crash data into traffic domains (TD) based on the speed limit, i.e., residential streets, city roads, arterial thoroughfares, rural roads, and intercity highways and calculate the crash speed distribution for each domain. The injury severity clustering is based on the ISS injury aggregator with cut-offs at 4, 9, and 16 for S1, S2, and S3, respectively. We estimated the 90th-percentiles of the S-parameter cut-offs with a 95% confidence level using the GIDAS accident database. The percentiles were calculated for the ADAS-equipped vehicle as well as for the crash opponent, stratified for crash type (front, oblique, side). The stratification had to be detailed further for side crashes as impact direction (near-side vs. far-side) and availability of a curtain airbag restraint system have a significant impact on injury severity. The application of the results towards the assessment of a crossing scenario is detailed in the discussion.

Population-based assessment of a vehicle fleet with seat belts providing lower shoulder belt forces than today.

Objective: The objective of this study was to quantify the population-based effects of a lower shoulder belt load limit on front row occupants in frontal car crashes. Method: Crashes of modern vehicles from the GIDAS (German In-Depth Accident Study) are corrected for bias and projected to the national level. Injury risk functions are computed for the injury severity levels Maximum Abbreviated Injury Scale (MAIS) 2+, MAIS 3+, and fatal, stratified by 2 age cohorts (16-44 years of age and 45 years or older). To assess the field effectivity of a "softer belt," the projected crash frequency data are modified separately for the 2 age cohorts such that its risk structure represents the risk of a softer belt. Given those 2 samples, the field effectivity of a softer belt is derived for several shares of the younger age cohort according to the injury severity levels MAIS 2+, MAIS 3+, and fatal. Results: The injury risk distribution of the projected crash frequency data, represented here by the injury risk functions obtained, fits well into the injury risk distribution of other data sets (Sweden, United States, and Japan) given in the literature. The relative effects of a lower belt force are stable over the different ratios of the younger and old age cohorts. At the MAIS 2+ level, a lower belt force can significantly reduce the number of injuries (about 10%). A lower belt force does not significantly affect the number of MAIS 3+ injuries. A lower belt force can, however, more than double the number of fatal injuries. Conclusions: Because the number of fatal injuries rises dramatically due to lower belt force, the reduction in the number of MAIS 2+ injuries comes at a very high cost. Therefore, whether reducing the belt force limit is the right approach is questionable.

Injury risk functions for frontal oblique collisions.

OBJECTIVE: The objective of this article was the construction of injury risk functions (IRFs) for front row occupants in oblique frontal crashes and a comparison to IRF of nonoblique frontal crashes from the same data set. METHOD: Crashes of modern vehicles from GIDAS (German In-Depth Accident Study) were used as the basis for the construction of a logistic injury risk model. Static deformation, measured via displaced voxels on the postcrash vehicles, was used to calculate the energy dissipated in the crash. This measure of accident severity was termed objective equivalent speed (oEES) because it does not depend on the accident reconstruction and thus eliminates reconstruction biases like impact direction and vehicle model year. Imputation from property damage cases was used to describe underrepresented low-severity crashes-a known shortcoming of GIDAS. Binary logistic regression was used to relate the stimuli (oEES) to the binary outcome variable (injured or not injured). RESULTS: IRFs for the oblique frontal impact and nonoblique frontal impact were computed for the Maximum Abbreviated Injury Scale (MAIS) 2+ and 3+ levels for adults (18-64 years). For a given stimulus, the probability of injury for a belted driver was higher in oblique crashes than in nonoblique frontal crashes. For the 25% injury risk at MAIS 2+ level, the corresponding stimulus for oblique crashes was 40 km/h but it was 64 km/h for nonoblique frontal crashes. CONCLUSIONS: The risk of obtaining MAIS 2+ injuries is significantly higher in oblique crashes than in nonoblique crashes. In the real world, most MAIS 2+ injuries occur in an oEES range from 30 to 60 km/h.