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This paper presents the final phase of a study to develop the modeling methodology for an advanced steering assembly with a safety-enhanced steering wheel and an adaptive energy absorbing steering column. For passenger cars built before the 1960s, the steering column was designed to control vehicle direction with a simple rigid rod. In severe frontal crashes, this type of design would often be displaced rearward toward the driver due to front-end crush of the vehicle. Consequently, collapsible, detachable, and other energy absorbing steering columns emerged to address this type of kinematics. These safety-enhanced steering columns allow frontal impact energy to be absorbed by collapsing or breaking the steering columns, thus reducing the potential for rearward column movement in severe crashes. Recently, more advanced steering column designs have been developed that can adapt to different crash conditions including crash severity, occupant mass/size, seat position, and seatbelt usage.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

Simulation based design optimization has become the common practice in automotive product development. Increasing computer models are developed to simulate various dynamic systems. Before applying these models for product development, model validation needs to be conducted to assess their validity. In model validation, for the purpose of obtaining results successfully, it is vital to select or develop appropriate metrics for specific applications. For dynamic systems, one of the key obstacles of model validation is that most of the responses are functional, such as time history curves. This calls for the development of a metric that can evaluate the differences in terms of phase shift, magnitude and shape, which requires information from both time and frequency domain. And by representing time histories in frequency domain, more intuitive information can be obtained, such as magnitude-frequency and phase-frequency characteristics.

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).

Efficient brain strain estimation is critical for routine application of a head injury model. Lately, a convolutional neural network (CNN) has been successfully developed to estimate spatially detailed brain strains instantly and accurately in contact sports. Here, we extend its application to automotive head impacts, where impact profiles are typically more complex with longer durations. Head impact kinematics (N=458) from two public databases were used to generate augmented impacts (N=2694). They were simulated using the anisotropic Worcester Head Injury Model (WHIM) V1.0, which provided baseline elementwise peak maximum principal strain (MPS). For each augmented impact, rotational velocity (vrot) and the corresponding rotational acceleration (arot) profiles were concatenated as static images to serve as CNN input.

The relationship between designing for both rigid fixed barrier (RFB) and vehicle-to-vehicle tests is a topical area of research. Specifically, vehicle-to-vehicle compatibility has been a topic of keen interest to many researchers, and the interplay between the two aspects of design is presently addressed. In this paper, the studied vehicles for potential vehicle-to-vehicle impacts included: sport utility vehicles (SUVs), Pickups (PUs), and passenger cars. The SUV/PU-to-Car frontal impact tests were compared to those obtained from vehicle-to-rigid fixed barrier frontal impacts. Acceleration pulses at the B-pillar/rocker as well as dash and cabin intrusions were monitored and compared. Additionally, the energy distributions in SUV/PU-to-Car crash tests were compared to those of single vehicle-to-RFB tests. It was concluded from the analysis that vehicle weight and front-end stiffness were not always the overriding factors dictating performance.

Light truck vehicles (LTVs), sport utility vehicles (SUVs), and vans collectively make up a growing segment of the total automotive fleet sales, particularly in the United States. The National Highway Traffic Safety Administration (NHTSA) has identified this trend and has increased the extent of its research in vehicle-to-vehicle compatibility. Additionally, vehicle compatibility concerns have also been emphasized by International Harmonization Research Activity (IHRA). Accordingly, with intention to further enhance road safety, research in the area of crash compatibility between cars and LTVs in different crash configurations is of significant importance. This paper describes a part of ongoing research at Ford Motor Company to further investigate the effect of compatibility in SUV/LTV-to-Car crashes.