Search

Objective: Automated driving systems (ADS) are actively being deployed within the driving fleet. ADS are designed to safely navigate roadways, which entails an expectation of encountering varying degrees of potential conflict with other road users. The ADS design and evaluation process benefits from estimating injury severity probabilities for collisions that may occur. Current regression models in the literature are typically bespoke analyses involving targeted principal directions of force (PDOFs) and occupant positions. It is preferable to rely on injury severity models derived from a single source to provide a continuous function of risk for all planar collisions, while also accounting for specific vehicle and occupant characteristics. The novel feature of the proposed models is continuous, parametric injury risk surfaces that encompass the full spectrum of available United States field data. Methods: We used years 2001-2015 of the National Automotive Sampling System, Crashworthiness Data System (NASS-CDS) and years 2017-2019 of the Crash Investigation Sampling System (CISS) to estimate injury risk at the maximum abbreviated injury scale (MAIS) 3 and higher (3+) and 5 and higher (5+) levels for all adult occupants traveling in 2002 or newer passenger vehicles which were less than 10 years old at the time of the crash. The models account for occupant, vehicle, and crash characteristics. Interactions with vulnerable road users (e.g., pedestrian, bicyclist) were not considered. Results: We present statistical models suitable to predict injury in all non-rollover crashes at the maximum MAIS3+ and 5+ levels, and show that these models can be comparable to similar single scenario (e.g., frontal) crash models. We discuss challenges with imputing missing field data, and discuss handling of covariates that may not be known at the time of the crash. Conclusions: Collision severity assessment is a vital component of the ADS design process. We developed a novel injury risk function that can assess occupant injury risks across the spectrum of foreseeable planar collisions. These models can provide insight on potential outcomes of counterfactual simulations, injury risk and crashworthiness considerations for human driven vehicles, and provide an evaluation tool that can be applied in ADS safety impact evaluation.

The relationship between laboratory and on-field performance of football helmets was assessed for 31 football helmet models selected from those worn by players in the 2015-2019 National Football League (NFL) seasons. Linear impactor tests were conducted with helmets placed on an instrumented Hybrid III head and neck assembly mounted on a sliding table. Based on impacts to each helmet at six impact locations and three velocities, a helmet performance score (HPS) was calculated using a linear combination of the head injury criterion (HIC) and the diffuse axonal multi-axis general evaluation (DAMAGE). To determine the on-field performance of helmets, helmet model usage, player participation, and incident concussion data were collected from the five NFL seasons and used to calculate helmet model-specific concussion rates. Comparison of laboratory HPS to the helmet model-specific concussion rates on a per play basis showed a positive correlation (r 2  = 0.61, p < 0.001) between laboratory and on-field performance of helmet models, indicating that helmets which exhibited reduced impact severity in the laboratory tests were also generally associated with lower concussion rates on-field. Further analysis showed that NFL-prohibited helmet models exhibited a significantly higher odds of concussion (OR 1.24; 95% CI 1.04-1.47; p = 0.017) relative to other helmet models.

Background: Concussions in American football remain a high priority of sports injury prevention programs. Detailed video review provides important information on causation, the outcomes of rule changes, and guidance on future injury prevention strategies., Purpose: Documentation of concussions sustained in National Football League games played during the 2015-2016 and 2016-2017 seasons, including consideration of video views unavailable to the public., Study Design: Descriptive epidemiology study., Methods: All reported concussions were reviewed with all available video footage. Standardized terminology and associated definitions were developed to describe and categorize the details of each concussion., Results: Cornerbacks sustained the most concussions, followed by wide receivers, then linebackers and offensive linemen. Half (50%) of concussions occurred during a passing play, 28% during a rushing play, and 21% on a punt or kickoff. Tackling was found to be the most common activity of concussed players, with the side of the helmet the most common helmet impact location. The distribution of helmet impact source-the object that contacted the concussed player's helmet-differed from studies of earlier seasons, with a higher proportion of helmet-to-body impacts (particularly shoulder) and helmet-to-ground impacts and with a lower proportion of helmet-to-helmet impacts. Helmet-to-ground concussive impacts were notable for the high prevalence of impacts to the back of the helmet and their frequency during passing plays., Conclusion: Concussion causation scenarios in the National Football League have changed over time., Clinical Relevance: The results of this study suggest the need for expanded evaluation of concussion countermeasures beyond solely helmet-to-helmet test systems, including consideration of impacts with the ground and with the body of the opposing player. It also suggests the possibility of position-specific countermeasures as part of an ongoing effort to improve safety.

The most important factor in predicting the risk of injury or death in a frontal crash is the crash severity, which is expressed as the velocity change, or delta-V, experienced by the vehicle during the crash. The National Automotive Sampling System (NASS) is the largest database in the world linking injury outcomes with delta-Vs, which are obtained from field reconstructions. The accuracy of these reconstructions was assessed by analyzing 228 NASS cases involving single event frontal crashes in which the vehicle's frontal delta-V was also measured directly by an onboard event data recorder (EDR). Compared to the EDR measurements, the delta-V values in NASS averaged 19% lower with a standard deviation of 8.6 kph. The effect of this error on injury and fatality risk calculations was investigated using NASS data from 1997 - 2006 for frontal crashes with a known delta-V. Injury and fatality risk functions were calculated by curve fitting the distributions of the delta-V values associated with injury and fatality incidence normalized by the fitted crash exposure distribution. Individual delta-V values were linearly scaled to correct for the bias error, and the delta-V distributions were corrected for scatter error using a numerical deconvolution technique. Correcting for delta-V bias error shifted the calculated risk curves to the right and correcting for delta-V scatter error shifted the curves back to the left, but to a lesser extent. The effects of occupant age, gender, and belt use on injury and fatality risk were substantial.