Stapp car crash journal最新文献

Objective: Previous studies have reported disparity in injuries between male and female drivers in the risk of certain types of injuries in frontal crashes that may be due to a myriad of sex-related differences, including body size, shape, anatomy, or sitting posture. The objectives of this study are 1) to use mesh-morphing methods to generate a diverse set of human body models (HBMs) representing a wide range of body sizes and shapes for both sexes, 2) conduct population-based frontal crash simulations, and 3) explore adaptive restraint design strategies that may lead to enhanced safety for the whole population while mitigating potential differences in injury risks between male and female drivers.

Method: A total of 200 HBMs with a wide range of body sizes and shapes were generated by morphing the THUMS v4.1 midsize male model into geometries predicted by the statistical human geometry models. Ten male and ten female HBMs were selected for population-based simulations. An existing automated simulation framework was leveraged to rapidly set up crash simulations with the morphed HBMs and previously-validated driver compartment and restraint models. A total of 1,000 frontal crash simulations were performed under varied restraint designs and crash severities. A surrogate model was developed based on the simulation data using a Gaussian Process (GP) method. Two design optimization schemes were used to flexibly adjust design parameters based on subject variables to minimize population injury risks while minimizing differences in injury risk between male and female HBMs.

Key results: The simulations indicated that the joint injury probability (Pjoint) is more sensitive to the seatbelt and driver airbag variables at 35 mph, while the variability is greatly reduced at 25 mph for all design variables. The optimal adaptive design strategy from these models suggested a higher seat belt load limit, higher airbag inflation pressure, smaller airbag venting, and higher steering column force for occupants with higher body mass index (BMI). The adaptive design reduced the population Pjoint by 19.6%, 31.8% and 38.8% from the baseline design when Delta-V equals to 25, 30 and 35 mph, respectively. For high speed crashes (Delta-V = 35 mph), the proposed adaptive design reduced the average Pjoint differences between men and women from 24.02% to 2.84% compared to the baseline design. Surprisingly, a restraint strategy constrained to sex-based balance is able to maintain similar injury risks between male and female drivers.

Major conclusion: This study is the first to integrate finite element crash simulations with adaptive restraint design optimization to potentially reduce population injury risks and safety balance between male and female occupants. Gaussian process was shown to be an effective surrogate to FE simulations.

Sonar sensor systems have been developed to prevent collisions between vehicles and surrounding objects by employing ultrasonic sensors mounted at the front of the vehicle. These systems warn drivers when nearby obstacles are detected. However, relatively few studies have examined the capacity of sonar to detect humans. This study aims to clarify the human detection capacity of front sonar sensors installed in two light passenger cars (LPC-I and LPC-II), one small passenger car (SPC), and one minivan (MNV). The LPC-I, SPC, and MNV were equipped with center and corner sensors, whereas the LPC-II had only corner sensors. Three volunteers-a child, an adult female, and an adult male-participated in the study. Human detectability was assessed using the "maximum detection distance ratio," defined as the ratio of the maximum detection distance for a volunteer to that for a standard pipe. The results showed that both the center and corner sensors consistently detected front- and side-facing human volunteers. For front-facing human volunteers, the maximum detection distance ratios relative to the pipe were 99-101% (child), 93-101% (adult female), and 98-101% (adult male) for the center sonar sensor, and 99-102%, 94-102%, and 96-100% for the corner sensor. For side-facing human volunteers, the corresponding ratios were 97-100%, 92-97%, and 94-99% for the center sensor, and 95-99%, 91-98%, and 93-98% for the corner sensor. These detection ratios were closely aligned with those of the pipe. These findings suggest that front sonar sensors can effectively detect humans prior to vehicle motion initiation, indicating their potential to reduce low-speed vehicle collisions with nearby pedestrians.

Belt-positioning booster seats (BPBs) help promote proper seat belt fit for children in vehicles. The effectiveness of BPBs depends on occupant posture, which can be influenced by BPB design features. This study aimed to quantitatively describe how children's postures naturally change over time in BPBs, using pressure mats. Thirty children aged 5 to 12 participated in two 30-minute trials using randomly assigned seating configurations. Five configurations were studied by installing two backless BPBs in vehicle captain's chairs, varying booster profile (high, low, or no BPB) and armrest presence (with or without BPB/vehicle seat armrests). TekScan 5250 pressure mats were placed on the seating surfaces. Children began in an ideal reference posture, and center of force (COF) data were collected continuously. Additional observations on posture, behavior, and comfort were periodically collected. Mixed models, including effects of seating configuration, time, and volunteer characteristics, were used to explore changes in COF position from the reference position with time. Children assumed a variety of postures. Over time, children showed a statistically significant forward COF shift of 2.5 cm from the initial posture across all trials (p = 0.003). No significant differences were found in the average COF position or translation between seating configurations in the fore-aft (x) or inboard-outboard (y) directions. However, the maximum and cumulative COF translation in the x-direction was significantly influenced by booster profile, with high-profile configurations resulting in the least amount of translation. Children tended to slouch over time, as evidenced by an average forward COF translation of 2.5 cm over thirty minutes. These findings were supported by video footage and posture data. Trends toward forward COF translation were most apparent in low-profile and no booster configurations. Such changes in booster occupant postures can imply increased injury risk, specifically associated with submarining as evaluated in previous computational investigations. Future research should examine these trends in real-world driving environments and assess how specific BPB design elements may support better long-term posture during vehicle travel.

The proportion of pedestrian injuries in motor-vehicle-crash-induced injuries in the U.S. has been increasing in recent years. Although extensive police-reported data on pedestrian injuries is available, the incomplete nature of the crash and injury information in these datasets presents a significant challenge for statistical injury analysis and pedestrian protection research. This study aims to address this issue by combining simulation data and field data to impute critical missing crash information in pedestrian crash cases through machine learning techniques. A total of 9,000 MADYMO simulations were generated using maximal projection design, incorporating variables such as pedestrian demographics, crash conditions, and vehicle impact parameters. Gaussian process (GP) surrogate models were trained to predict injury risks with simulation parameters calibrated using the complete crash information in the Pedestrian Crash Data Study (PCDS) dataset. Maximum likelihood estimations were then employed to impute the missing vehicle speed in Linked Michigan Trauma dataset. Validation involved comparing the imputed vehicle speed distribution with that of the PCDS dataset and verifying four CIREN cases reconstructed by both the proposed method and a physics-based approach. The histogram of the reconstructed vehicle speeds in Linked Michigan Trauma dataset highly correlated with that from the PCDS dataset. In the four CIREN cases, the absolute deviation between the reconstruction vehicle speeds from the proposed method and physics-based approach was 9 kph on average, with the predicted injury risks matched the observed AIS levels. These results support the use of machine learning for reconstructing missing crash data and enhancing pedestrian injury risk modeling.

Prevention of rear-impact neck injuries remains challenging for safety designers due to a lack of understanding of the tissue-level response and injury risk. Soft tissue injuries have been inferred from clinical, cadaveric, and numerical studies; however, there is a paucity of data for neck muscle injury, commonly reported as muscle pain. The goal of this study was to investigate the effect of muscle pre-tension and activation on muscle strain and injury risk resulting from low-severity rear impacts using a detailed finite element head and neck model (HNM). The HNM was extracted from the GHBMC average stature male model and re-postured to match a volunteer study, with measured T1 kinematics applied as boundary conditions to the HNM. Three cases were simulated for three impact severities: the baseline repostured HNM, the HNM including muscle pre-tension, and the HNM with muscle pre-tension and muscle activation. The head kinematics, vertebral kinematics, muscle strains, and three neck injury criteria were calculated to assess injury risk. The kinematic response of the neck model demonstrated an S-shaped pattern, followed by extension in the rear impact cases. The maximum kinetics, kinematics, and muscle strains occurred later in the impact during the extension phase. The distribution and magnitude of muscle strain depended on muscle pre-tension and activation, and the largest predicted strains occurred at locations associated with muscle injury reported in the literature. The HNM with muscle pre-tension and muscle activation provides a tool to assess rear impact response and could inform injury mitigation strategies in the future.

This research investigated injury risk functions (IRF) for the THOR-AV 50th percentile male dummy in accordance with ISO TS18506, focusing on areas with design changes. The IRF development utilized a combination of physical tests and finite element (FE) model simulations. For certain postmortem human subject test cases lacking physical dummy tests, the validated Humanetics THOR-AV FE model (v0.7.2) was used to quickly generate data, with the understanding that final IRFs based on full physical test data might offer greater accuracy. Log-logistic, log-normal, and Weibull survival functions were fitted with 95% confidence intervals. The Akaike Information Criterion, Goodman-Kruskal-Gamma, Area under the Curve of Receiver Operating Characteristic, and Quantile-Quantile plot were employed to assess the prediction strength and relative quality of the final IRF selections. Among the three survival distributions, the Weibull distribution provided the best fit. The lumbar Fz was identified as the best indicator for lumbar spine injury, followed by Lij. The Fz injury risk values at 5%, 25%, and 50% probabilities are 2170N, 3560N, and 4856N for MAIS2+, respectively. The Lij injury risk values at 5%, 25%, and 50% probabilities are 0.44, 0.65, and 0.79 for MAIS2+, respectively. Abdomen pressure from APTS sensors was found to be a weak indicator for abdomen injury prediction, with injury risk values at 5%, 25%, and 50% probabilities being 128, 209, and 268 kPa for MAIS2+, respectively. The total ASIS force from the left and right ASIS load cells was a better injury predictor than the maximum ASIS load from the individual load cells, with injury risk values at 5%, 25%, and 50% probabilities being 542, 1872, and 3522 Newtons for MAIS2+, respectively.

Recent studies have found that Brain Injury Criteria (BrIC) grossly overpredicts instances of real-world, severe traumatic brain injury (TBI). However, as it stands, BrIC is the leading candidate for a rotational head kinematics-based brain injury criteria for use in automotive regulation and general safety standards. This study attempts to understand why BrIC overpredicts the likelihood of brain injury by presenting a comprehensive analysis of live primate head impact experiments conducted by Stalnaker et al. (1977) and the University of Pennsylvania before applying these injurious conditions to a finite element (FE) monkey model. Data collection included a thorough analysis and digitization of the head impact dynamics and resulting pathology reports from Stalnaker et al. (1977) as well as a representative reconstruction of the Penn II baboon diffuse axonal injury (DAI) model. Computational modeling techniques were employed on a FE Rhesus monkey model, first introduced by Arora et al. (2019), to derive risk related brain tissue strain thresholds from the laboratory data. The existing critical velocities proposed for BrIC were then scaled until the target strain level associated with each severity level of diffuse brain injury was reproduced in the FE model of the human brain. Overall, this study provides a comprehensive understanding of these two historical non-human primates (NHP) models and predicts a strain based diffuse tissue injury threshold (MPS99.9) of 1.0 and 1.6 for concussion (mild TBI) and DAI (severe TBI), respectively. The findings indicate scale factors of 1.6 to 5.9 times the original BrIC critical velocities, depending on the loading duration, are required to predict severe (AIS 4+) diffuse brain injury. These results allude to a necessity for including angular acceleration and duration as kinematic parameters in an injury criterion that can accurately predict real-world, diffuse brain injuries. This study also attempts to evaluate and recommend a methodology for post-processing strain parameters produced by head models, settling on the use of MPS99.9 and CSDM50.

Current voluntary standards for wheelchair crashworthiness only test under frontal and rear impact conditions. To help provide an equitable level of safety for occupants seated in wheelchairs under side impact, we developed a sled test procedure simulating nearside impact loading using a fixed staggered loading wall. Publicly available side impact crash data from vehicles that could be modified for wheelchair use were analyzed to specify a relevant crash pulse. Finite element modeling was used to approximate the side impact loading of a wheelchair during an FMVSS No. 214 due to vehicle intrusion. Validation sled tests were conducted using commercial manual and power wheelchairs and a surrogate wheelchair base fixture. Test procedures include methods to position the wheelchair to provide consistent loading for wheelchairs of different dimensions. The fixture and procedures can be used to evaluate the integrity of wheelchairs under side impact loading conditions.

The development of drones has raised questions about their safety in case of high-speed impacts with the head. This has been recently studied with dummies, postmortem human surrogates and numerical models but questions are still open regarding the transfer of skull fracture tolerance and procedures from road safety to drone impacts. This study aimed to assess the performance of an existing head FE model (GHBMC M50-O v6.0) in terms of response and fracture prediction using a wide range of impact conditions from the literature (low and high-speed, rigid and deformable impactors, drones). The fracture prediction capability was assessed using 156 load cases, including 18 high speed tests and 19 tests for which subject specific models were built. The GHBMC model was found to overpredict peak forces, especially for rigid impactors and fracture cases. However, the model captured the head accelerations tendencies for drone impacts. The formulation of bone elements, the failure representation and the scalp material properties were found of interest for future investigation. The model still predicted a sizable proportion of skull fractures. With failure enabled, it reached a sensitivity of 86.6% and a specificity of 82.0% (n=156). With failure disabled, risk curves with a rating of good according to ISO/TS 18506:2014 were developed using the second principal strain in the outer table cortical solid elements.

This study presents an analysis of 364 motorcycle helmet impact tests, including standard certified full-face, open-face, and half-helmets, as well as non-certified (novelty) helmet designs. Two advanced motorcycle helmet designs that incorporate technologies intended to mitigate the risk of rotational brain injuries (rTBI) were included in this study. Results were compared to 80 unprotected tests using an instrumented 50th percentile Hybrid III head form and neck at impact speeds ranging from 6 to 18 m/s (13 to 40 mph). Results show that, on average, the Head Injury Criterion (HIC) was reduced by 92 percent across certified helmets, compared to the unhelmeted condition, indicating substantial protection against focal head and brain injuries. However, findings indicate that standard motorcycle helmets increase the risk of AIS 2 to 5 rotational brain injuries (rTBI) by an average of 30 percent compared to the unprotected condition, due to the increased rotational inertia generated by the added size and weight of the helmet. Advanced helmets performed, on average, about 5 percent better than standard certified helmets. Non-certified or novelty helmets offer inadequate protection against focal head and brain injuries, though they may offer some insight into rTBI protection. The findings of this study also indicate a critical methodological deficiency in the oblique impact tests utilized in revised motorcycle helmet standards, including ECE 22.06, Snell M2025, and FRHPe-02, which fail to correctly assess rTBI risk. This paper provides recommendations for enhancing motorcycle helmet design to improve protection against rotational traumatic brain injuries.