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The debate surrounding roof deformation and occupant injury potential has existed in the automotive community for over 30 years. In analysis of real-world rollovers, assessment of roof deformation and occupant compartment space starts with the post-accident roof position. Dynamic movement of the roof structure during a rollover sequence is generally acknowledged but quantification of the dynamic roof displacement has been limited. Previous assessment of dynamic roof deformation has been generally limited to review of the video footage from staged rollover events. Rollover testing for the evaluation of injury potential has typically been studied utilizing instrumented test dummies, on-board and off-board cameras, and measurements of residual crush. This study introduces an analysis of previously undocumented real-time data to be considered in the evaluation of the roof structure's dynamic behavior during a rollover event.

Micro-mobility is a fast-growing trend in the transportation industry with stand-up electric scooters (e-scooters) becoming increasingly popular in the United States. To date, there are over 350 ride-share e-scooter programs in the United States. As this popularity increases, so does the need to understand the performance capabilities of these vehicles and the associated operator kinematics. Scooter tip-over stability is characterized by the scooter geometry and controls and is maintained through operator inputs such as body position, interaction with the handlebars, and foot placement. In this study, testing was conducted using operators of varying sizes to document the capabilities and limitations of these e-scooters being introduced into the traffic ecosystem. A test course was designed to simulate an urban environment including sidewalk and on-road sections requiring common maneuvers (e.g., turning, stopping points, etc.) for repeatable, controlled data collection.

The operational safety of automated driving system (ADS)-equipped vehicles (AVs) needs to be quantified for an understanding of risk, requiring the measurement of parameters as they relate to AVs and human driven vehicles alike. In prior work by the Institute of Automated Mobility (IAM), operational safety metrics were introduced as part of an operational safety assessment (OSA) methodology that provide quantification of behavioral safety of AVs and human-driven vehicles as they interact with each other and other road users. To calculate OSA metrics, the data capture system must accurately and precisely determine position, velocity, acceleration, and geometrical relationships between various safety-critical traffic participants. The design of an infrastructure-based system that is intended to capture the data required for calculation of OSA metrics is addressed in this paper.

A significant portion of real-world passenger vehicle side impacts occur at lower speeds than testing conducted by the National Highway Traffic Safety Administration (NHTSA) or the Insurance Institute for Highway Safety (IIHS). Test data from low- to moderate-speed side impacts involving late-model passenger vehicles is limited, making the evaluation of vehicle impact response, occupant loading, and injury potential challenging. This study provides the results of low- to moderate-speed impact testing involving a late-model mid-size sedan. Two full-scale Non-Deformable Moving Barrier (NDMB) side impact crash tests were conducted at speeds of 6.2 mph (10.0 kph) and 13.4 mph (21.6 kph). Instrumentation on the late-model sedan used for the test series included tri-axis accelerometers and seat belt load cells.

As a result of the development of Event Data Recorders (EDR) and the recent FMVSS regulation 49 CFR 563, today’s automobiles provide a limited subset of electronic data measurements of a vehicle’s state before and during a crash. Prior to this data, the only information available about the vehicle movements before or during a collision had come from physical evidence (e.g. tire marks), witnesses, aftermarket camera systems on vehicles, and ground-based cameras that were monitoring vehicle traffic or used for security surveillance. Today’s vehicles equipped with Advanced Driver Assistance Systems (ADAS) have vehicle-based sensors that measure information about the environment around a vehicle including other vehicles, pedestrians, and fixed wayside objects.

This study reports pedal activation forces and typical acceleration and deceleration rates during everyday driving activities. Twenty subjects of varying ages, height and weight participated in the study. Each subject was asked to drive a four-door sedan along 2.3 miles of roadway in DuPage County, Illinois. Vehicle speed, acceleration, and position were measured using a global positioning system that was synchronized with force data collected from load cells rigidly mounted on the vehicle's accelerator and brake pedals. Pedal forces and vehicle behavior were measured during common driving tasks such as, shifting the transmission into reverse, backing out of a parking spot, and, making a right hand turn from a stop sign. Our data suggests that simple vehicle dynamic tasks produced in experimental settings may not reliably reproduce vehicle and occupant behavior.

Many published studies have characterized walking and running speeds of young children. However, there is a paucity of data on the cycling speeds of very young children (4 to 5 years old). The purpose of this study was to obtain an estimate of cycling speed for boys and girls both who are learning to ride bicycles (i.e., younger children who still ride with training wheels) and who have already learned to ride bicycles (i.e., slightly older children who no longer use training wheels). A sample of 32 child riders (17 boys, 15 girls; 17 four-year-olds who still ride with training wheels, 15 five-year-olds who do not) were asked to ride a short pre-defined distance at their usual speed when riding, and again at their highest speed. We found that while age and experience can differentiate riders, there were only small differences between boys’ and girls’ speeds in either age group.

The biomechanical injury assessment for an occupant in a planar vehicle-to-vehicle collision often requires a kinematic analysis of impact-related occupant motion. This analysis becomes more complex when the collision force is eccentric to the center of gravity on a struck vehicle because the vehicle kinematics include both translation and potentially significant yaw rotational rates. This study examines the significance of vehicle yaw on occupant kinematics in eccentric (off-center) planar collisions. The paper describes the calculation of the instantaneous center of rotation (ICR) in a yawing vehicle post-impact and explores how mapping this quantity may inform an occupant’s trajectory when using a free particle “occupant” analysis. The study initially analyzed the impact-related occupant motion for all the outboard seat positions in a minivan using several hypothetical examples of eccentric vehicle-to-vehicle crash configurations with varying PDOF, delta-V, and yaw rate.

When two vehicles contact in a side swipe collision, if one of the vehicles has lug nuts that protrude outboard of the wheel, gouge marks may be left in the other vehicle's exterior. Hereafter the marked vehicle will be referred to as the car and the vehicle with the lug nuts will be called the truck. If the car is stationary, the marks will be in the form of a curtate trochoid and will reveal no information about the speed of the truck. If the car is not stationary, analysis of the shape of the traces left on the side of the car by the lug nuts of the rotating truck wheel can determine the velocity ratio of the car to that of the truck. If the speed of either vehicle is known or can be determined using other information or accident reconstruction techniques, lug nut mark analysis will enable determination of the other vehicle's speed.

This study assesses the exposure distribution and injury rate (MAIS 4+F) to front-outboard non-ejected occupants by crash severity, belt use and head restraint type and damage in rear impacts using 1997-2015 NASS-CDS data. Rear crashes with a delta V <24 km/h (15 mph) accounted for 71% of all exposed occupants. The rate of MAIS 4+F increased with delta V and was higher for unbelted than belted occupants with a rate of 11.7% ± 5.2% and 6.0% ± 1.5% respectively in 48+ km/h (30 mph) delta V. Approximately 12% of front-outboard occupants were in seats equipped with an integral head restraint and 86% were with an adjustable head restraint, irrespective of crash severity. The overall injury rate was 0.14% ± 0.05% and 0.22% ± 0.06%, respectively. It was higher in cases where the head restraint was listed as “damaged”. Thirteen cases involving a lap-shoulder belted occupant in a front-outboard seat in which “damage” to the adjustable head restraint was identified.

A significant number of vehicle-to-vehicle collisions involve front-to-rear impacts at low- to moderate-speeds. While a variety of studies have been conducted since the 1990s involving fore-aft collisions, those discussing the response of late model passenger vehicles during progressively more severe impacts are limited. In this study, four inline, front-rear tests were conducted using two midsize sedans of the same make, model, and year. An instrumented Hybrid III 50th percentile-male Anthropomorphic Test Device (ATD) was located in the driver seat of each sedan and was restrained using the standard three-point seat belt system. Instrumentation on the vehicles included tri-axis accelerometers and seat belt load cells. For each test, the centerlines of the vehicles were aligned, and the striking vehicle impacted the stationary target vehicle at closing speeds of 4.6, 7.9, 13.5, and 20.9 mph (7.4, 12.7, 21.7, and 33.6 kph).

Prior to developing or modifying the protocol of a performance evaluation test, it is important to identify field relevant conditions. The objective of this study was to assess the distribution of selected crash variables from rear crash field collisions involving modern vehicles. The number of exposed and serious-to-fatally injured non-ejected occupants was determined in 2008+ model year (MY) vehicles using the NASS-CDS and CISS databases. Selected crash variables were assessed for rear crashes, including severity (delta V), impact location, struck vehicle type, and striking objects. In addition, 15 EDRs were collected from 2017 to 2019 CISS cases involving 2008+ MY light vehicles with a rear delta V ranging from 32 to 48 km/h. Ten rear crash tests were also investigated to identify pulse characteristics in rear crashes. The tests included five vehicle-to-vehicle crash tests and five FMVSS 301R barrier tests matching the struck vehicle.

As micromobility devices (i.e., e-bikes, scooters, skateboards, etc.) continue to increase in popularity, there is a growing need to test these devices for varying purposes such as performance assessment, crash reconstruction, and design of new products. Although tests have been conducted across the industry for electric scooters (e-scooters), this paper describes a novel method for crash testing e-scooters with anthropomorphic test devices (ATDs) “riding” them, providing new sources for data collection and research. A sled fixture was designed utilizing a pneumatic crash rail to propel the scooters with an overhead gantry used for stabilization of the ATD until release just prior to impact. The designed test series included impacts with a 5.5-inch curb at varying incidence angles, a stationary vehicle, or a standing pedestrian ATD. Test parameter permutations included changing e-scooter tire sizes, impact speeds, and rider safety equipment.

Field data was analyzed on second-row children in front, side and rear impacts to study fatality trends by model year (MY) and calendar year (CY) with 1980-2020 MY vehicles. The different MY and CY perspectives show changes in rates that are useful for setting priorities for second-row child safety in rear impacts. 1990 to 2019 FARS was queried to assess the number of fatally injured and non-ejected second-row children (0-15 years old) in crashes without fires. The children included outboard occupants seated behind an occupied front seat and center occupants. The data was analyzed for rear, front and side impacts to assess crash frequency. 1990-2015 POLK was queried to assess exposure of registered vehicles and estimate a fatality rate. The FARS and POLK data were sub-grouped by MY of the vehicle and CY of the crash. There were 2.8-times more fatally injured children in frontal crashes than in the rear crashes. The ratio of frontal and rear crashes varied with CY sub-groups.

Extensive testing has been conducted to evaluate both the dynamic response of vehicle structures and occupant protection systems in rollover collisions though the use of Anthropomorphic Test Devices (ATDs). Rollover test methods that utilize a fixture to initiate the rollover event include the SAE2114 dolly, inverted drop tests, accelerating vehicle body buck on a decelerating sled, ramp-induced rollovers, and Controlled Rollover Impact System (CRIS) Tests. More recently, programmable steering controllers have been used with sedans, vans, pickup trucks, and SUVs to induce a rollover, primarily for studying the vehicle kinematics for accident reconstruction applications. The goal of this study was to create a prototypical rollover crash test for the study of vehicle dynamics and occupant injury risk where the rollover is initiated by a steering input over realistic terrain without the constraints of previously used test methods.