Validation of a booted finite element model of the WIAMan ATD lower limb in component and whole-body vertical loading impacts with an assessment of the boot influence model on response

Objective: A novel anthropomorphic test device (ATD) representative of the 50th percentile male soldier is being developed to predict injuries to a vehicle occupant during an underbody blast (UBB). The main objective of this study was to develop and validate a finite element (FE) model of the ATD lower limb outfitted with a military combat boot and to insert the validated lower limb into a model of the full ATD and simulate vertical loading experiments.

Methods: A Belleville desert combat boot model was assigned contacts and material properties based on previous experiments. The boot model was fit to a previously developed model of the barefoot ATD. Validation was performed through 6 matched pair component tests conducted on the Vertically Accelerated Loads Transfer System (VALTS). The load transfer capabilities of the FE model were assessed along with the force-mitigating properties of the boot. The booted lower limb subassembly was then incorporated into a whole-body model of the ATD. Two whole-body VALTS experiments were simulated to evaluate lower limb performance in the whole body.

Results: The lower limb model accurately predicted axial loads measured at heel, tibia, and knee load cells during matched pair component tests. Forces in booted simulations were compared to unbooted simulations and an amount of mitigation similar to that of experiments was observed. In a whole-body loading environment, the model kinematics match those recorded in experiments. The shape and magnitude of experimental force–time curves were accurately predicted by the model. Correlation between the experiments and simulations was backed up by high objective rating scores for all experiments.

Conclusion: The booted lower limb model is accurate in its ability to articulate and transfer loads similar to the physical dummy in simulated underbody loading experiments. The performance of the model leads to the recommendation to use it appropriately as an alternative to costly ATD experiments.     

Motorcycle crashes potentially preventable by three crash avoidance technologies on passenger vehicles

Objective: The objective of this study was to identify and quantify the motorcycle crash population that would be potential beneficiaries of 3 crash avoidance technologies recently available on passenger vehicles.

Methods: Two-vehicle crashes between a motorcycle and a passenger vehicle that occurred in the United States during 2011–2015 were classified by type, with consideration of the functionality of 3 classes of passenger vehicle crash avoidance technologies: frontal crash prevention, lane maintenance, and blind spot detection. Results were expressed as the percentage of crashes potentially preventable by each type of technology, based on all known types of 2-vehicle crashes and based on all crashes involving motorcycles.

Results: Frontal crash prevention had the largest potential to prevent 2-vehicle motorcycle crashes with passenger vehicles. The 3 technologies in sum had the potential to prevent 10% of fatal 2-vehicle crashes and 23% of police-reported crashes. However, because 2-vehicle crashes with a passenger vehicle represent fewer than half of all motorcycle crashes, these technologies represent a potential to avoid 4% of all fatal motorcycle crashes and 10% of all police-reported motorcycle crashes.

Discussion: Refining the ability of passenger vehicle crash avoidance systems to detect motorcycles represents an opportunity to improve motorcycle safety. Expanding the capabilities of these technologies represents an even greater opportunity. However, even fully realizing these opportunities can affect only a minority of motorcycle crashes and does not change the need for other motorcycle safety countermeasures such as helmets, universal helmet laws, and antilock braking systems.     

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.     

Evaluation of geometrically personalized THUMS pedestrian model response against sedan–pedestrian PMHS impact test data

Objective: Evaluating the biofidelity of pedestrian finite element models (PFEM) using postmortem human subjects (PMHS) is a challenge because differences in anthropometry between PMHS and PFEM could limit a model's capability to accurately capture cadaveric responses. Geometrical personalization via morphing can modify the PFEM geometry to match the specific PMHS anthropometry, which could alleviate this issue. In this study, the Total Human Model for Safety (THUMS) PFEM (Ver 4.01) was compared to the cadaveric response in vehicle–pedestrian impacts using geometrically personalized models.

Methods: The AM50 THUMS PFEM was used as the baseline model, and 2 morphed PFEM were created to the anthropometric specifications of 2 obese PMHS used in a previous pedestrian impact study with a mid-size sedan. The same measurements as those obtained during the PMHS tests were calculated from the simulations (kinematics, accelerations, strains), and biofidelity metrics based on signals correlation (correlation and analysis, CORA) were established to compare the response of the models to the experiments. Injury outcomes were predicted deterministically (through strain-based threshold) and probabilistically (with injury risk functions) and compared with the injuries reported in the necropsy.

Results: The baseline model could not accurately capture all aspects of the PMHS kinematics, strain, and injury risks, whereas the morphed models reproduced biofidelic response in terms of trajectory (CORA score = 0.927 ± 0.092), velocities (0.975 ± 0.027), accelerations (0.862 ± 0.072), and strains (0.707 ± 0.143). The personalized THUMS models also generally predicted injuries consistent with those identified during posttest autopsy.

Conclusions: The study highlights the need to control for pedestrian anthropometry when validating pedestrian human body models against PMHS data. The information provided in the current study could be useful for improving model biofidelity for vehicle–pedestrian impact scenarios.     

Modeling traffic air pollution in street canyons in New York City for intra-urban exposure assessment in the US Multi-Ethnic Study of atherosclerosis and air pollution

We evaluated the Danish AirGIS air quality and exposure model system using air quality measurement data from New York City in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Measurements were used from three US EPA Air Quality System (AQS) monitoring stations and a comprehensive MESA Air measurement campaign including about 150 different locations and about 650 samples of about 2 week measurements of NO_x, NO₂ and PM_2.5. AirGIS is a deterministic exposure model system based on the dispersion models Operational Street Pollution Model (OSPM) and the Urban Background Model (UBM). The UBM model reproduced the annual levels within 1–26% depending on station and pollutant at the three urban background EPA monitor stations, and generally reproduced well the seasonal and diurnal variation. The full model with OSPM and UBM reproduced the MESA Air measurements with a correlation coefficient of r² = 0.51 for NO_x, r² = 0.28 for NO₂ and r² = 0.73 for PM_2.5.     

Absence behavior as traffic crash predictor in bus drivers

Problem

Various indicators of health have been shown to be associated with traffic crash involvement. As general health is also related to absence from work, the latter variable may be more strongly related to crashes, especially for professional drivers.

Method

Bus driver absence from work was analyzed in association with their crash records. Two British samples and one Swedish sample were used.

Results

One of the British samples yielded fair correlations between crash record and absence, while for the other the effect was restricted to the first three months of driving. The Swedish data had effects in the expected direction but these were not significant.

Discussion

The use of an indirect, overall measurement of health, may be a viable method for predicting the traffic crash involvement for professional drivers, although replications are needed in larger samples and other populations.

Impact on industry

The use of absence records for the identification of at risk drivers would seem to be a simple and useful method for companies with major fleets, and it also shows the importance of promoting employee health and well being at work as a potential method of reducing the cost, not only of absenteeism, but also of crashes in company vehicles.     

Seat Belt Use in States and Territories with Primary and Secondary Laws -- United States, 2006

Problem

Motor-vehicle crashes are a leading cause of death in the United States. In the event of a crash, seat belts are highly effective in preventing serious injury and death.

Methods

Data from the 2006 Behavioral Risk Factor Surveillance System were used to calculate prevalence of seat belt use by state and territory and by type of state seat belt law (primary vs. secondary enforcement).

Results

In 2006, seat belt use among adults ranged from 58.3% to 91.9% in the states and territories. Seat belt use was 86.0% in states and territories with primary enforcement laws and 75.9% in states with secondary enforcement laws.

Discussion

Seat belt use continues to increase in the United States. Primary enforcement laws remain a more effective strategy than secondary enforcement laws in getting motor-vehicle occupants to wear their seat belts.     

Integrated model of simulated occupant injury risk and real medical costs

Introduction

The purpose of this study was to develop an integrated methodology that links occupant injury risk functions, estimated in the laboratory, with real world medical treatment costs by using the abbreviated injury score (AIS). Using our model, the expected medical treatment costs for crash injuries to various body regions and of different severities can be investigated.

Methods

First, the simulation results are compared with NHTSA crash data. We used a modified kinematics simulation model that incorporates an F = E^b function as a supplement to the previous Steffan's model to obtain a more accurate acceleration history a(t). Second, head injury criteria HIC₃₆ can be calculated from a(t), and we use the injury probability P as a function of HIC_36, as proposed by Kuppa, to obtain the injury risk function for various AIS values. Third, medical treatment cost models for various AIS values can be calculated by using a regression cost model with real world data. Finally, the injury risk function and medical treatment cost models are linked through AIS values. We establish an integrated methodology and predict medical costs and car safety data using real world police reports, medical treatment costs, and laboratory simulation results.

Results

Using head injuries in frontal crashes as an example, we focus on simulation parameters for different vehicle models, with and without airbags. We specifically examine impact closing speed, Delta-V, and impact directions.

Conclusion

Simulation results can be used to supplement insufficient real crash data, in particular ΔV, and injury risk results from police crash reports.

Impact on industry

The proposed integrated methodology may provide the vehicle industry with a new safety assessment method. Real crash data coupling provides consumers with more realistic and applicable information.     

Committing driving violations: An observational study comparing city, town and village

Introduction

This article compares observed driving behavior in a city, a town, and a village.

Method

Unobtrusive observations were made at intersections in each residential type. Five violation types were observed: (a) not wearing a seat belt (seat belt violation); (b) not using a safety seat for a child (safety seat violation for children); (c) not using a speaker while speaking on the phone (on-phone violation); (d) failing to comply with a ‘give way’ sign (‘give way’ sign violation); and (e) stopping in an undesignated area (undesignated stop violation). It was expected that in accordance with the anonymity hypothesis that the bigger residential areas' rate of traffic violations would be higher. The effects of the residential type, drivers' gender, and age were assessed using the multiple regression model. The stepwise method of evaluation was employed. The model converged on step 3 (Adjusted R square = 0.039). Residential type and gender contributed significantly to the model. Results: Consistent with prior research, male drivers committed more violations than female drivers. Chi-square analyses were used to test the distribution of violations by the settlement types. Overall, more drivers committed violations in the two small residential areas than in the city, with 30% of city drivers, 43% of town drivers, and 51% of village drivers committing at least one violation (χ2 (2) = 37.65, p < 0.001). Moreover, in the town and the village, a combination of one or more violations was committed more often than in the city (χ2 (1) = 34.645, p < 0.001). Accordingly, more drivers committed violations in the two small settlements (48.4%) than in the city (30.6%). Possible explanations for the observed results were provided in the Discussion section.

Impact on Industry

The conclusions of this paper are that drivers in small villages tend to disobey traffic laws. Therefore, efforts have to be made in companies to take this issue in consideration while running fleets in companies located in small places far from the center.