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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 National Highway Traffic Safety Administration (NHTSA) published a Request for Comments (RFC) on proposed changes to the New Car Assessment Program (NCAP) in 2015 and 2017. One potential change was the introduction of a frontal Oblique Impact (OI) crash test. The Test device for Human Occupant Restraint (THOR) in the front left seat was used in the proposed OI test. The motivations behind the current study were a) determine if OI sled tests can be simplified, b) study the sensitivity of the THOR chest deflection to the shoulder belt layout in OI and c) assess the NHTSA-proposed THOR thoracic injury risk curves. In the current study, eleven oblique sled impact tests were conducted. The environment was representative of a generic mid-sized sedan. The buck was mounted on a rigid plate that allowed the pre-test rotation of the buck relative to the sled axis. A generic mid-sized OI pulse was used. The pulse was applied in the longitudinal direction of the sled.

In December 2015, the National Highway Traffic Safety Administration (NHTSA) published a Request for Comments on proposed changes to the New Car Assessment Program (NCAP). One potential change is the addition of a frontal oblique impact (OI) crash test using the Test Device for Human Occupant Restraint (THOR). The resultant acetabulum force, which is a unique and specifically defined in the THOR dummy, will be considered as a new injury metric. In this study, the results of ten OI tests conducted by NHTSA on current production mid-sized vehicles were investigated. Specifically, the test data was used to study the lower extremity kinematics for the driver and front passenger THOR dummies. It was found that the acetabulum force patterns varied between the driver and passenger and between the left leg and the right leg of the occupants. The maximum acetabulum force can occur either on the left side or right side of a driver or a front passenger in an OI event.

On December 2015, The National Highway Traffic Safety Administration (NHTSA) published its proposal to implement U.S New Car Assessment Program (NCAP) changes covering three categories of crashworthiness, crash avoidance and pedestrian protection, beginning with the 2019 model year. The crashworthiness category included a new frontal oblique impact (OI) test protocol. The test compromises of a new Oblique Moving Deformable Barrier (OMDB), new THOR 50th percentile male (THOR-50M) anthropomorphic test device (ATD), and a new test configuration. An OMDB of 2,486 kg (5,480 lb) impacts a stationary target vehicle at a speed of 90 kph (56 mph) at an angle of 15 degrees with a 35% barrier overlap with the front end of the target vehicle. In vehicle-to-vehicle collisions, the lighter weight vehicle experience higher velocity change and higher acceleration levels, thereby, occupants in the lighter vehicle experience higher injury risk.

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 objective of this paper focused on the modeling of an adaptive energy absorbing steering column which is the first phase of a study to develop a modeling methodology for an advanced steering wheel and column assembly. Early steering column designs often consisted of a simple long steel rod connecting the steering wheel to the steering gear box. In frontal collisions, a single-piece design steering column would often be displaced toward the driver as a result of front-end crush. Over time, engineers recognized the need to reduce the chance that a steering column would be displaced toward the driver in a frontal crash. As a result, collapsible, detachable, and other energy absorbing steering columns emerged as safer steering column designs. The safety-enhanced construction of the steering columns, whether collapsible, detachable, or other types, absorb rather than transfer frontal impact energy.

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.

With a goal to help develop pressure sensor calibration and deployment algorithms using computer simulations, an Arbitrary Lagrangian Eulerian (ALE) approach was adopted in this research to predict the responses of side crash pressure sensors for unitized vehicles. For occupant protection, acceleration-based crash sensors have been used in the automotive industry to deploy restraint devices when vehicle crashes occur. With improvements in the crash sensor technology, pressure sensors that detect pressure changes in door cavities have been developed recently for vehicle crash safety applications. Instead of using acceleration (or deceleration) in the acceleration-based crash sensors, the pressure sensors utilize pressure change in a door structure to determine the deployment of restraint devices. The crash pulses recorded by the acceleration-based crash sensors usually exhibit high frequency and noisy responses.

In an attempt to assist pressure sensor algorithm and calibration development using computer simulations, an Arbitrary Lagrangian Eulerian (ALE) approach was adopted in this study to predict the responses of side crash pressure sensors for body-on-frame vehicles. Acceleration based, also called G-based, crash sensors have been used extensively to deploy restraint devices, such as airbags, curtain airbags, seatbelt pre-tensioners, and inflatable seatbelts, in vehicle crashes. With advancements in crash sensor technologies, pressure sensors that measure pressure changes in vehicle side doors have been developed recently and their applications in vehicle crash safety are increasing. The pressure sensors are able to detect and record the dynamic pressure change when the volume of a vehicle door changes as a result of a crash.

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).

This paper reports a 2010 study undertaken to determine generic acceleration pulses for testing and evaluating advanced batteries for application in electric passenger vehicles. These 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. The crash test data were gathered from the following test modes and sources: 1 Frontal rigid flat barrier test at 35 mph (NHTSA NCAP) 2 Frontal 40% offset deformable barrier test at 40 mph (IIHS) 3 Side moving deformable barrier test at 38 mph (NHTSA side NCAP) 4 Side oblique pole test at 20 mph (US FMVSS 214/NHTSA side NCAP) 5 Rear 70% offset moving deformable barrier impact at 50 mph (US FMVSS 301). The accelerometers used were from locations in the vehicle where deformation is minor or non-existent, so that the acceleration represents the “rigid-body” motion of the vehicle.

Two rigid rollover test devices were constructed to have the approximate dimensions, mass and inertial properties of a mid-sized Car and Sport Utility Vehicle (SUV). The rigid devices were used to generate vehicle and occupant responses from a series of laboratory rollover tests. For each rigid rollover test, a deceleration sled was used to subject each rigid vehicle to nearly identical lateral speeds and decelerations. The rigid vehicles were limited to a single roll by tethering the vehicles to the deceleration cart. The vehicle's roll rate, roll angle, lateral acceleration and Anthropomorphic Test Devices (ATD) neck responses generated from the rigid SUV were compared to the responses of a full vehicle production SUV under similar test conditions. The rigid SUV and Car devices were then used to examine the effects of activating safety belt pre-tensioning systems and roof mounted side curtain airbags at various times on ATD neck forces and moments.

Changes in vehicle safety design technology and the increasing use of seat-belts and airbag restraint systems have gradually changed the relative proportion of lower extremity injuries. These changes in real world injuries have renewed interest and the need of further investigation into occupant injury mechanisms and biomechanical impact responses of the knee-thigh-hip complex during frontal impacts. This study uses a detailed finite element model of the human body to simulate occupant knee impacts experienced in frontal crashes. The human body model includes detailed anatomical features of the head, neck, shoulder, chest, thoracic and lumbar spine, abdomen, pelvis, and lower and upper extremities. The material properties used in the model for each anatomic part of the human body were obtained from test data reported in the literature. The human body model used in the current study has been previously validated in frontal and side impacts.

This study applies a detailed finite element model of the human body to simulate occupant knee impacts experienced in vehicular frontal crashes. The human body model includes detailed anatomical features of the head, neck, chest, thoracic and lumbar spine, abdomen, and lower and upper extremities. The material properties used in the model for each anatomic part of the human body were obtained from test data reported in the literature. The total human body model used in the current study has been previously validated in frontal and side impacts. Several cadaver knee impact tests representing occupants in a frontal impact condition were simulated using the previously validated human body model. Model impact responses in terms of force-time and acceleration-time histories were compared with test results. In addition, stress distributions of the patella, femur, and pelvis were reported for the simulated test conditions.

This paper explores the accuracy of a planar, impulse-momentum impact model in representing the dynamics of three vehicle-to-ground impacts that occurred during a SAE J2114 dolly rollover test. The impacts were analyzed, first, using video analysis techniques to obtain the actual velocity conditions, accelerations, impact force components and the energy loss for each of the impacts. Next, these same impacts were analyzed using the known initial velocity conditions and the subject impact model. The equations of this impact model yielded calculated values for the velocity changes and energy loss for each impact. These calculated results were then compared to the actual dynamics data from the video analysis of the impacts to determine the accuracy of the impact model results. For all three vehicle-to-ground impacts considered in this study, the impact model results for the velocity changes and energy loss showed excellent agreement with the video analysis results for these parameters.

This paper examines the use of camera-matching video analysis techniques to quantify the vehicle dynamics and deformation for a dolly rollover test run in accordance with the SAE Recommended Practice J2114. The method presented enables vehicle motion data and deformation measurements to be obtained without the use of the automated target tracking employed by existing motion tracking systems. Since it does not rely on this automated target tracking, the method can be used to analyze video from rollover tests which were not setup in accordance with the requirements of these automated motion tracking systems. The method also provides a straightforward technique for relating the motion of points on the test vehicle to the motion of the vehicle's center-of-mass. This paper, first, describes the specific rollover test that was utilized. Then, the camera-matching method that was used to obtain the vehicle motion data and deformation measurements is described.

A computational model of a mid-size sport utility vehicle was developed using MADYMO. The model includes a detailed description of the suspension system and tire characteristics that incorporated the Delft-Tyre magic formula description. The model was correlated by simulating a vehicle suspension kinematics and compliance test. The correlated model was then used to simulate a J-turn vehicle dynamics test maneuver, a roll and non-roll ditch test, corkscrew ramp and a lateral trip test, the results of which are presented in this paper. The results indicate that MADYMO is able to reasonably predict the vehicle and occupant responses in these types of applications and is potentially suited as a tool to help setup a suite of vehicle configurations and test conditions for rollover sensor testing. A suspension system sensitivity study is presented for the laterally tripped non-roll event.

An experimental study was conducted using a dynamic rollover component test system (ROCS) to study the effects of activating a pyro-mechanical buckle pre-tensioner and an electric retractor on the driver and front right passenger head and pelvis excursions. The ROCS is a unique system capable of producing vehicle responses that replicate four distinct phases of a tripped rollover: trip initiation, roll initiation, free-flight vehicle rotation, and vehicle to ground contact. This component test system consists of a rigid occupant compartment derived from a mid-size SUV with complete 1st row seating and interior trim, a simulated vehicle suspension system and an elastic vehicle-to-ground-contact surface. The ROCS system was integrated with a Deceleration Rollover Sled (DRS). Dynamic responses of the ROCS system, including both the rigid compartment and occupant, were measured and recorded.

The biofidelity of the Ford Motor Company human body finite element (FE) model in side impact simulations was analyzed and evaluated following the procedures outlined in ISO technical report TR9790. This FE model, representing a 50th percentile adult male, was used to simulate the biomechanical impact tests described in ISO-TR9790. These laboratory tests were considered as suitable for assessing the lateral impact biofidelity of the head, neck, shoulder, thorax, abdomen, and pelvis of crash test dummies, subcomponent test devices, and math models that are used to represent a 50th percentile adult male. The simulated impact responses of the head, neck, shoulder, thorax, abdomen, and pelvis of the FE model were compared with the PMHS (Post Mortem Human Subject) data upon which the response requirements for side impact surrogates was based. An overall biofidelity rating of the human body FE model was determined using the ISO-TR9790 rating method.

This paper presents an image analysis of a laboratory-based rollover crash test using camera-matching photogrammetry. The procedures pertaining to setup, analysis and data process used in this method are outlined. Vehicle roll angle and rate calculated using the method are presented and compared to the measured values obtained using a vehicle mounted angular rate sensor. Areas for improvement, accuracy determination, and vehicle kinematics analysis are discussed. This paper concludes that the photogrammetric method presented is a useful tool to extract vehicle roll angle data from test video. However, development of a robust post-processing tool for general application to crash safety analysis requires further exploration.