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Recent field data have shown that the occupant protection in vehicle rear seats failed to keep pace with advances in the front seats likely due to the lack of advanced safety technologies. The objective of this study was to optimize advanced restraint systems for protecting rear seat occupants with a range of body sizes under different frontal crash pulses. Three series of sled tests (baseline tests, advanced restraint trial tests, and final tests), MADYMO model validations against a subset of the sled tests, and design optimizations using the validated models were conducted to investigate rear seat occupant protection with 4 Anthropomorphic Test Devices (ATDs) and 2 crash pulses.

Recent field data have shown that the occupant protection in vehicle rear seats failed to keep pace with advances in the front seats likely due to the lack of advanced safety technologies. The objective of this study was to optimize advanced restraint systems for protecting rear seat occupants with a range of body sizes under different frontal crash pulses. Three series of sled tests (baseline tests, advanced restraint trial tests, and final tests), MADYMO model validations against a subset of the sled tests, and design optimizations using the validated models were conducted to investigate rear seat occupant protection with 4 Anthropomorphic Test Devices (ATDs) and 2 crash pulses.

Occupant stature and body shape may have significant effects on injury risks in motor vehicle crashes, but the current finite element (FE) human body models (HBMs) only represent occupants with a few sizes and shapes. Our recent studies have demonstrated that, by using a mesh morphing method, parametric FE HBMs can be rapidly developed for representing a diverse population. However, the biofidelity of those models across a wide range of human attributes has not been established. Therefore, the objectives of this study are 1) to evaluate the accuracy of HBMs considering subject-specific geometry information, and 2) to apply the parametric HBMs in a sensitivity analysis for identifying the specific parameters affecting body responses in side impact conditions. Four side-impact tests with two male post-mortem human subjects (PMHSs) were selected to evaluate the accuracy of the geometry and impact responses of the morphed HBMs.

The objective of this study is to develop a method that uses a combination of field data analysis, naturalistic driving data analysis, and computational simulations to explore the potential injury reduction capabilities of integrating passive and active safety systems in frontal impact conditions. For the purposes of this study, the active safety system is actually a driver assist (DA) feature that has the potential to reduce delta-V prior to a crash, in frontal or other crash scenarios. A field data analysis was first conducted to estimate the delta-V distribution change based on an assumption of 20% crash avoidance resulting from a pre-crash braking DA feature. Analysis of changes in driver head location during 470 hard braking events in a naturalistic driving study found that drivers’ head positions were mostly in the center position before the braking onset, while the percentage of time drivers leaning forward or backward increased significantly after the braking onset.

The aging population is a growing concern as the increased fragility and frailty of the elderly results in an elevated incidence of injury as well as an increased risk of mortality and morbidity. To assess elderly injury risk, age-specific computational models can be developed to directly calculate biomechanical metrics for injury. The first objective was to develop an older occupant Global Human Body Models Consortium (GHBMC) average male model (M50) representative of a 65 year old (YO) and to perform regional validation tests to investigate predicted fractures and injury severity with age. Development of the GHBMC M50 65 YO model involved implementing geometric, cortical thickness, and material property changes with age. Regional validation tests included a chest impact, a lateral impact, a shoulder impact, a thoracoabdominal impact, an abdominal bar impact, a pelvic impact, and a lateral sled test.

Lateral impact tests were performed using seven male post-mortem human subjects (PMHS) to characterize the force-deflection response of contacted body regions, including the lower abdomen. All tests were performed using a dual-sled, side-impact test facility. A segmented impactor was mounted on a sled that was pneumatically accelerated into a second, initially stationary sled on which a subject was seated facing perpendicular to the direction of impact. Positions of impactor segments were adjusted for each subject so that forces applied to different anatomic regions, including thorax, abdomen, greater trochanter, iliac wing, and thigh, could be independently measured on each PMHS. The impactor contact surfaces were located in the same vertical plane, except that the abdomen plate was offset 5.1 cm towards the subject.

This study outlines a protocol for image data collection acquired from human volunteers. The data set will serve as the foundation of a consolidated effort to develop the next generation full-body Finite Element Analysis (FEA) models for injury prediction and prevention. The geometry of these models will be based off the anatomy of four individuals meeting extensive prescreening requirements and representing the 5th and 50th percentile female, and the 50th and 95th percentile male. Target values for anthropometry are determined by literature sources. Because of the relative strengths of various modalities commonly in use today in the clinical and engineering worlds, a multi-modality approach is outlined. This approach involves the use of Computed Tomography (CT), upright and closed-bore Magnetic Resonance Imaging (MRI), and external anthropometric measurements.

Development and validation of crash test dummies and computational models that are capable of predicting the risk of injury to all parts of the knee-thigh-hip (KTH) complex in frontal impact requires knowledge of the force transmitted from the knee to the hip under knee impact loading. To provide this information, the knee impact responses of whole and segmented cadavers were measured over a wide range of knee loading conditions. These data were used to develop and help validate a computational model, which was used to estimate force transmitted to the cadaver hip. Approximately 250 tests were conducted using five unembalmed midsize male cadavers. In these tests, the knees were symmetrically impacted with a 255-kg padded impactor using three combinations of knee-impactor padding and velocity that spanned the range of knee loading conditions produced in FMVSS 208 and NCAP tests. Each subject was tested in four conditions.

The ease of getting into and out of passenger cars and light trucks is a critical component of customer acceptance and product differentiation. In commercial vehicles, the health and safety of drivers is affected by the design of the steps and handholds they use to get into and out of the cab. Ingress/egress assessment appears to represent a substantial application opportunity for digital human models. The complexity of the design space and the range of possible biomechanical and subjective measures of interest mean that developing useful empirical models is difficult, requiring large-scale subject testing with physical mockups. Yet, ingress and egress motions are complex and strongly affected by the geometric constraints and driver attributes, posing substantial challenges in creating meaningful simulations using figure models.

Standing reach envelopes are important tools for the design of industrial and vehicle environments. Previous work in this area has focussed on manikin-based (where a few manikins are used to simulate individuals reaching within the region of interest) and population-based (where data are gathered on many individuals reaching in a constrained environment) approaches. Each of these methods has merits and shortfalls. The current work bridges the manikin- and population-based approaches to assessing reach by creating population models using kinematic simulation techniques driven by anthropometric data. The approach takes into account body dimensions, balance, and postural cost to create continuous models that can be used to assess designs with respect to both maximal and submaximal reaches. Cost is quantified as the degree to which the torso is involved in the reach, since the inclination of the torso is a good measure of lower-back load and may be related to subjective reach difficulty.

This paper (1) summarizes previous human factors/safety research on parking (8 studies, mostly over 20 years old), (2) provides statistics for 10,400 parking-related crashes in Michigan from 2000-2002, and (3) summarizes interviews with 6 insurance agents concerning parking crashes. These sources indicate: 1 About 1/2 to 3/4 of parking crashes involve backing, often into another moving vehicle while emerging from a parking stall. 2 Eight-and-a-half foot-wide stalls had higher crash rates than wider stalls. 3 Most parallel parking crashes occur on major streets, not minor streets. 4 Lighting and driver impairment were minor factors in parking crashes.

In the mid 1970s, UMTRI investigated the biomechanical properties of the head and neck using 180 “normal” adult subjects selected to fill eighteen subject groups based on age (young, mid-aged, older), gender, and stature (short, medium, and tall by gender). Lateral-view radiographs of the subjects’ cervical spines and heads were taken with the subjects seated in a simulated automotive neutral posture, as well as with their necks in full-voluntary flexion and full-voluntary extension. Although the cervical spine and lower head geometry were previously measured manually and documented, new technologies have enabled computer digitization of the scanned x-ray images and a more comprehensive and detailed analysis of the variation in cervical spine and lower head geometry with subject age, stature, and gender. After scanning the radiographic images, 108 skeletal landmarks on the cervical vertebrae and 10 head landmarks were digitized.

Late model passenger cars and light trucks incorporate occupant protection systems with airbags and knee restraints. Knee restraints have been designed principally to meet the unbelted portions of FMVSS 208 that require femur load limits of 10-kN to be met in barrier crashes up to 30 mph, +/- 30 degrees utilizing the 50% male Anthropomorphic Test Device (ATD). In addition, knee restraints provide additional lower-torso restraint for belt-restrained occupants in higher-severity crashes. An analysis of frontal crashes in the University of Michigan Crash Injury Research and Engineering Network (UM CIREN) database was performed to determine the influence of vehicle, crash and occupant parameters on knee, thigh, and hip injuries. The data sample consists of drivers and right front passengers involved in frontal crashes who sustained significant injuries (Abbreviated Injury Scale [AIS] ≥ 3 or two or more AIS ≥ 2) to any body region.

Various means for measuring a vehicle's roll stability performance are considered in terms of the pertinence of their test conditions to the rollover crash record, the practicality and quality of the measurement, and their ability to span the performance range of the population of light-duty vehicles. Classical static measures as well as the so-called “maneuver-type” tests that have been under extensive study by the U.S. Dept. of Transportation are specifically addressed. In light of limitations facing the existing methods, the concept of a centrifuge test device is introduced and discussed. The apparatus is comprised of a relatively large machine that mounts a full-sized vehicle tangent to the rotation of a radial arm which revolves at a controlled angular rate. The minimum steady speed of rotation that induces a rollover response in the mounted vehicle corresponds to the static rollover threshold, in units of lateral (or centripetal) acceleration.

Effective application of human figure models to truck interior design requires accurate data on the postures and positions of truck drivers. Errors in positioning of figure models propagate to errors in reach, visibility, and other analyses. This paper describes methods used in a recent study to measure in-vehicle driving postures in Class 6, 7, and 8 trucks. A three-dimensional coordinate measurement machine was used to measure body landmark locations after a driver completed a short road course. The data were used to validate posture-prediction models developed in a previous laboratory study. Vehicle calibration, driver selection, and testing methods are reviewed.

Most human figure models used in ergonomic analyses present postural comfort ratings based on joint angles, and present a single comfort score for the whole body or on a joint-by-joint basis. The source data for these ratings is generally derived from laboratory studies that link posture to ratings. Lacking in many of these models is a thorough treatment of the distribution of ratings for the population of users. Information about ratings distributions is necessary to make cost-effective tradeoffs when design changes affect subjective responses. This paper presents experimental and analytic methods used to develop distribution models for incorporating subjective rating data in ergonomic assessments.

Few studies have systematically examined the effects of truck and bus workstation geometry on driver posture and position. This paper presents methods for determining drivers' postural responses and preferred component locations using a reconfigurable vehicle mockup. Body landmark locations recorded using a three-dimensional digitizer are used to compute a skeletal-linkage representation of the drivers' posture. A sequential adjustment procedure is used to determine the preferred positions and orientations of key components, including the seat, steering wheel, and pedals. Data gathered using these methods will be used to create new design tools for trucks and buses, including models of driver-selected seat position, eye location, and needed component adjustment ranges. The results will also be used to create accurate posture-prediction models for use with human modeling software.

Recent research in the ASPECT (Automotive Seat and Package Evaluation and Comparison Tools) program has led to the development of a new method for automobile driver posture prediction, known as the Cascade Model. The Cascade Model uses a sequential series of regression functions and inverse kinematics to predict automobile occupant posture. This paper presents an alternative method for driver posture prediction using data-guided kinematic optimization. The within-subject conditional distributions of joint angles are used to infer the internal cost functions that guide tradeoffs between joints in adapting to different vehicle configurations. The predictions from the two models are compared to in-vehicle driving postures.

An easy-to-use computer program for ride analysis was recently developed. The result of this effort-RideSim- predicts time history responses, power spectral density (PSD) functions, and a driver oriented measure of ride comfort. RideSim employs a graphical user interface (called SGUI, for simulation graphical user interface) to control data preparation, simulation execution, animation, and data analysis. The SGUI allows the user to operate the program by pointing and clicking with a mouse, rather than by using cumbersome text commands. It also manages the vehicle dynamics parameters, the resulting simulation output, and results of post-processing analyses (i.e., PSD analysis). The vehicle dynamics model was generated with the AUTOSIM multibody dynamics program. This program uses Kane’s Method and computer algebra to create a parametric dynamics simulation that can be easily linked to the SGUI.

A new method of predicting automobile occupant posture is presented. The Cascade Prediction Model approach combines multiple independent predictions of key postural degrees of freedom with inverse kinematics guided by data-based heuristics. The new model, based on posture data collected in laboratory mockups and validated using data from actual vehicles, produces accurate posture predictions for a wide range of passenger car interior geometries. Inputs to the model include vehicle package dimensions, seat characteristics, and occupant anthropometry. The Cascade Prediction Model was developed to provide accurate posture prediction for use with any human CAD model, and is applicable to many vehicle design and safety assessment applications.