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This paper presents the analysis of a series of frontal crash tests conducted by the Insurance Institute of Highway Safety that are commonly referred to as Small Overlap Impacts (SOI). The occurrence and severity of such frontal impacts in the real world were estimated using two different methods. Both methods used the National Automotive Sampling Scheme (NASS), which is a stratified sample of crashes in the US. The first method utilized an algorithm commonly known as Frontal Impact Taxonomy (FIT). The second method was based on comparison of deformation patterns of vehicles involved in frontal crashes in the NASS data files with those produced in tests conducted by the IIHS. FIT analysis of the data indicate that approximately 7.5% of all 11-1 o'clock frontal crashes in NASS are represented by the IIHS SOI test condition and they account for 6.1% of all serious-to-fatal injuries to front seat occupants restrained by seat belts and airbags.

A theoretical, mathematical, risk assessment procedure was developed to estimate the fraction of drivers that incurred head and thoracic AIS3+ injuries in full-engagement frontal crashes. The estimates were based on numerical simulations of various real-world events, including variations of crash severity, crash speed, level of restraint, and occupant size. The procedure consisted of four steps: (1) conduct the simulations of the numerous events, (2) use biomechanical equations to transform the occupant responses into AIS3+ risks for each event, (3) weight the maximum risk for each event by its real-world event frequency, and (4) sum the weighted risks. To validate the risk assessment procedure, numerous steps were taken. First, a passenger car was identified to represent average field performance.

An age-dependent, serious-to-fatal (AIS3+), thoracic risk curve was derived and evaluated for frontal impacts. The study consisted of four parts. In Part 1, two datasets of post mortem human subjects (PMHS) were generated for statistical and sensitivity analyses. In Part 2, logistic regression analyses were conducted. For each dataset, two statistical methods were applied: (1) a conventional maximum likelihood method, and (2) a modified maximum likelihood method. Therefore, four statistical models were derived — one for each dataset/statistical method combination. For all of the resulting statistical models (risk curves), the linear combination of maximum normalized sternum deflection and age of the PMHS was identified as a feasible predictor of AIS3+ thoracic injury probability. In Part 3, the PMHS-based risk curves were transformed into test-dummy-based risk curves. In Part 4, validation studies were conducted for each risk curve.

A new inflator specification, the “inflator thrust variable,” was developed to better explain measured mid-sized male, instrumented test dummy responses in the chest-on-module test condition. Specifically, controlled laboratory experiments were conducted with non-production, driver airbag modules with inflators of various outputs and gas constituents in an effort to assess their effects on a pertinent occupant response. Regression analyses showed that the inflator thrust variable is a better predictor of the observed variation in peak viscous criterion responses than either peak tank pressure or the related pressure rise rate when inflators of differing gas composition were compared.

This paper describes improvements made to the injury risk curves for peak neck tension, peak neck extension moment and a linear combination of tension and extension moment that produce peak stress in the anterior-longitudinal ligament at the head-to-neck junction. Data from previously published experiments that correlated neck injuries to 10-week-old, anesthetized pigs and neck response measurements of a 3-year-old child dummy that were subjected to similar airbag deployments are updated and used to generate Normal probability curves for the risk of AIS ≥ 3 neck injury for the 3-year-old child. These curves are extended to other sizes and ages by normalizing for neck size. Factors for percent of muscle tone and ligamentous failure stress as a function of age are incorporated in the risk analysis. The most sensitive predictor of AIS ≥ 3 neck injury for this data set is peak neck tension.