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The objective of this study is to analytically model the fatality risk in frontal vehicle-to-vehicle crashes of the current vehicle fleet, and its sensitivity to vehicle mass change. A model is built upon an empirical risk ratio-mass ratio relationship from field data and a theoretical mass ratio-velocity change ratio relationship dictated by conservation of momentum. The fatality risk of each vehicle is averaged over the closing velocity distribution to arrive at the mean fatality risks. The risks of the two vehicles are summed and averaged over all possible crash partners to find the societal mean fatality risk associated with a subject vehicle of a given mass from a fleet specified by a mass distribution function. Based on risk exponent and mass distribution from a recent fleet, the subject vehicle mean fatality risk is shown to increase, while at the same time that for the partner vehicles decreases, as the mass of the subject vehicle decreases.

Reliable testing of a mechanical system requires the procedures used for the evaluation to be repeatable and reproducible. However, it is never possible to exactly repeat or reproduce the tests that are used for evaluation. To overcome this limitation, a statistical evaluation procedure can generally be used. However, most of the statistical procedures use scalar values as input without the ability to handle vectors or time-histories. To overcome these limitations, two numerical/statistical methods for determining if the impact time-history response of a mechanical system is repeatable or reproducible are evaluated and elaborated upon. Such a system could be a vehicle, a biological human surrogate, an Anthropometric Test Device (ATD or dummy), etc. The responses could be sets of time-histories of accelerations, forces, moments, etc., of a component or of the system. The example system evaluated is the BioRID II rear impact dummy.

This paper reports a study undertaken by the Crash Safety Working Group (CSWG) of the United States Council for Automotive Research (USCAR) to determine generic acceleration pulses for testing and evaluating advanced batteries subjected to inertial loading for application in electric passenger vehicles. These pulses were based on characterizing vehicle acceleration time histories from standard laboratory vehicle crash tests. Crash tested passenger vehicles in the United States vehicle fleet of the model years 2005-2009 were used in this study. Crash test data, in terms of acceleration time histories, were collected from various crash modes conducted by the National Highway Traffic Safety Administration (NHTSA) during their New Car Assessment Program (NCAP) and Federal Motor Vehicle Safety Standards (FMVSS) evaluations, and the Insurance Institute for Highway Safety (IIHS).

A data processing algorithm is presented for determining the spatial orientation and position of a rigid body in impact tests based on an instrumentation scheme consisting of a triaxial angular rate sensor and a trialaxial linear accelerometer. The algorithm adopts the unit quaternion as the main parameterized representation of the spatial orientation, and calculates its time history by solving an ordinary differential equation with the angular rate sensor reading as the input. Two supplemental representations, the Euler angles and the direction cosine matrix, are also used in this work, which provide an intuitive description of the orientation, and convenience in transforming the linear accelerometer output in the instrumentation frame to the global frame. The algorithm has been implemented as a computer program, and a set of example impact tests are included to demonstrate its application.

For the purposes of analyzing and understanding the general effects of a set of different vehicle attributes on overall crash outcome a fleet model is used. It represents the impact response, in a one-dimensional sense, of two vehicle frontal crashes, across the frontal crash velocity spectrum. The parameters studied are vehicle mass, stiffness, intrusion, pulse shape and seatbelt usage. The vehicle impact response parameters are obtained from the NCAP tests. The fatality risk characterization, as a function of the seatbelt use and vehicle velocity, is obtained from the NASS database. The fatality risk is further mapped into average acceleration to allow for evaluation of the different vehicle impact response parameters. The results indicate that the effects of all the parameters are interconnected and none of them is independent. For example, the effect of vehicle mass on fatality risk depends on seatbelt use, vehicle stiffness, available crush, intrusion and pulse shape.

Simple rigid body dynamics models are created to analyze the curb- and soil-trip types of rollover events and experimental methods that are used to simulate these events. Equations for the models are given, and they are integrated numerically to obtain the solution. Solutions of the models provide a break down of the energy during these events, which exposes the importance of energy absorption, unloading, and friction during the impact-and-roll process. Furthermore, the models are used to derive the critical sliding velocity under different test parameters. They are also used to understand near-critical state responses of the vehicle, and the corresponding characteristics of the signals in the phase space.

Developing an effective side impact ATD for assessing vehicle impact responses requires a method for evaluating that ATD's bio-fidelity. ISO/TR9790 has been in existence for some years to serve that purpose. Recently, NHTSA sponsored a research project on the post-mortem human subjects (PMHS) responses subjected to side impact conditions. Based on those newly available PMHS data, Maltese generated a new approach for creating bio-fidelity corridors for human surrogates. The approach incorporates the time factor into the evaluation equation and automates the process (Maltese et al. 2002). This paper serves as the first attempt to look closely at the new bio-fidelity corridor generation process (hereafter referred as the Maltese approach) with respect to its validity, effectiveness, as well as its practicality. The effect of mass scaling was first examined in order to ensure the integrity of the data. The time alignment scheme and the formation of the corridors were then tested.

The effects of vehicle “stiffness” and mass on the occupant response during a crash may be determined by evaluation of accident data. However, “stiffness” and mass may be correlated, making it difficult to separate their effects. In addition, a single-valued “stiffness”, although well defined for linear case, is not well defined for non-linear systems, such as in vehicle crash, making the separation task even more difficult. One approach to addressing the lack of a clear definition of stiffness is to use multiple definitions. Each stiffness definition can then be correlated with mass to look for trends. In this study, such an approach was taken, and the different stiffness definitions were given and their values were obtained from rigid barrier crash test data. No clear relationship between mass and stiffness appears to exist. All the stiffness measures reviewed show, at best, only a weak correlation with mass. A stiffness analysis among different vehicle types was also carried out.

A simple spring-mass model of the impact response of the side impact dummy (SID) is established. The spring and mass constants of the model are established through system identification methodology based on data from impact tests. The tests are performed in laboratory with hydraulically driven impactors impacting the chest and pelvis of the SID. The input data to the model consist of measured contact force or impactor velocity time histories, and the output data are accelerations on the rib, spine, and pelvis of the SID. The established model appears to predict the test results with reasonable accuracy. The main purpose of this study, however, is to use this simple model to carry out parametric studies of the response of the dummy with changing impact parameters, the result of which would be useful in understanding vehicle crash tests using the SID.

The response of the spine acceleration to rib and pelvis acceleration input of the side impact dummy (SID) is modeled using system identification methods. The basis for the modeling is a simplified representation of the SID by a 3-mass, 2-spring system. Based on this spring-mass representation, two types of response models are established. The first is a "gray-box" type with rib/pelvis-spine relationship modeled by Auto Regression with eXogeneous (or eXtra) input (ARX) type system models. The structure of these models is partially based on the spring-mass simplified representation, hence the notion "gray- box." The parameters of these models are identified through linear regression from test data. The second type of models is noted "physical model" here, since it is strictly a state- space form of the equation of motion of the simple spring-mass representation.

Supplemental airbag safety restraint systems are an integral part of today's vehicle package. This inflatable restraint technology relies heavily on woven fabrics and particularly on knowledge pertaining to a fabric's permeability as a function of pressure drop, inflation temperature of the gas and fabric weave. While fabric permeability can be quantified by actual experimental measurements, the number and non-linearity of the variables involved make the experiments time and cost intensive. Moreover, interpolations within a given data set yield questionable results. For these reasons a feed-forward artificial neural network (ANN) technique was utilized to predict fabric permeability. This is an interpretive procedure. An ANN routine must first be trained. During this training the ANN is introduced to actual cause and effect patterns with adjustments being made by changes in weighting factors until the errors in the output variables are minimized.

Mechanisms of thoraco-abdominal trauma were investigated utilizing unembalmed, repressurized human cadavers subjected to frontal impact with a steering wheel assembly. The focus of this research program was on trauma to the soft-tissue organs surrounded by the thoracic cage, as well as on the kinematic response of the thoracic cage. The results are compared to other thoraco-abdominal research programs conducted at the University of Michigan Transportation Research Institute (UMTRI) during the last eight years.

Heart-aortic trauma was investigated using live, anesthetized and postmortem canines subjected to frontal impact with a blunt impactor. The major focuses of this research program were: trauma to the heart aortic system, the kinematic response of the thoracic cage, and pressure in the ascending and descending aorta.

A test series using unembalmed cadavers was conducted to investigate thoracic response differences in lateral impacts between high energy (rib fractures produced) and low energy (no rib fractures produced) testing and also the response to low energy impacts for different impact directions (frontal, 45°, and lateral). Five of the test subjects were instrumented with a nine-accelerometer package and an eighteen-accelerometer array to measure thoracic response. Seven of the test subjects were instrumented with a triaxial accelerometer on the head and a six-accelerometer array to measure thoracic response. Impact events were performed with either the UMTRI pendulum impact device or the UMTRI pneumatic impact device. The subject was struck with a free-traveling mass (25 or 56 kg) which was fitted with either a 15 cm round or 20 cm square rigid metal surface.

Multiple axial knee impacts and/or a single lateral pelvis impact were performed on a total of 19 cadavers. The impacting surface was padded with various materials to produce different force-time and load distribution characteristics. Impact load and skeletal acceleration data are presented as functions of both time and frequency in the form of mechanical impedance. Injury descriptions based on gross autopsy are given. The kinematic response of the pelvis during and after impact is presented to indicate the similarities and differences in response of the pelvis for various load levels. While the impact response data cannot prescribe a specific tolerance level for the pelvis, they do indicate variables which must be considered and some potential problems in developing an accurate injury criterion.