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While various Advanced Driver Assistance System (ADAS) features have become more prevalent in passenger vehicles, their ability to potentially avoid or mitigate vehicle crashes has limitations. Due to current technological limitations, forward collision mitigation technologies such as Forward Collision Warning (FCW) and Automated Emergency Braking (AEB) lack the ability to consistently perform in many unique and challenging scenarios. These limitations are often outlined in driver manuals for ADAS equipped vehicles. One such scenario is the case of a stationary lead vehicle at the side of the road. This is generally considered to be a challenging scenario for FCW and AEB to address because it can often be difficult for the system to discern this threat accurately and consistently from non-threatening roadway infrastructure without unnecessary or nuisance system activations.

The increased prevalence of larger and more energy-dense battery packs for transportation and grid storage applications has resulted in an increasing number of severe battery thermal events. The implications on product reliability, consumer safety, and the surrounding environment are significant. While there are many potential root causes for battery thermal runaway, these events often start within a single battery cell or group of cells that cascade to neighboring cells and other combustible materials, rapidly increasing the hazard profile of the battery pack as more stored energy is released. Reducing these hazards requires preventing severe thermal runaway scenarios by mitigating cell-to-cell propagation through the improved design of both individual cells and battery packs.

The use of Lithium-ion batteries in the transportation sector has its own unique set of requirements such as high-power demands, cooling challenges, and risk of mechanical failure due to crashes. Active and passive components of thermal management systems in battery-powered products are designed to mitigate the effects of thermal runaway events and prevent cell-to-cell propagation. Designing safe battery-powered systems requires an understanding of how the battery pack will behave while undergoing thermal runaway, including critical data such as total energy yielded, rate of energy generation, as well as venting patterns and directions. Details such as thermal runaway energy fractions associated with the cell casing as well as vent gas and ejecta can be used to inform and optimize battery pack designs and the product as a whole. The NASA Fractional Thermal Runaway Calorimeter (FTRC) was created to measure these values.

Electric vehicles (EVs) and the development around them has been rapid in recent years. As the battery is the most essential component of an electric vehicle, a lot of research and analysis has been focused on ensuring safe and reliable performance of batteries. Considering the location, size, and operating conditions for EV batteries, they must be designed with an in-built safety infrastructure keeping in mind certain realistic scenarios such as fire exposure, mechanical vibration, collisions, over-charging, single cell failures, and others. In this paper, we discuss an overview of various EV battery failure mechanisms, present current safety and abuse testing methods and standards associated with such mechanisms and discuss the need for the development and implementation of additional testing standards to better characterize the safety performance of EV battery packs.

As the deployment of automated vehicles (AVs) on public roadways expands, there is growing interest in establishing metrics that can be used to evaluate vehicle operational safety. The set of Operational Safety Assessment (OSA) metrics, that include several safety envelope-type metrics, previously proposed by the Institute of Automated Mobility (IAM) are a step towards this goal. The safety envelope OSA metrics can be computed using kinematics derived from video data captured by infrastructure-based cameras and thus do not require on-board sensor data or vehicle-to-infrastructure (V2I) connectivity, though either of the latter data sources could enhance kinematic data accuracy. However, the calculation of some metrics includes certain vehicle-specific parameters that must be assumed or estimated if they are not known a priori or communicated directly by the vehicle.

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.

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.

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.

Safety-critical systems in most modern applications are designed in a way such that they provide a fail-safe operation when a fault occurs, to pose minimum risk to the user. As these systems become more sophisticated with increased functionality, it is important that their design incorporates functional safety concepts which entail detection of a potential harmful condition that prompts a corrective action to prevent hazardous events. In this paper, we discuss the significance of safety-critical systems along with the implementation of fail-safe designs in these systems. We also aim to provide an overview of functional safety as addressed in some of the industry standards and through a case study demonstrate how the concepts can be used when developing a safety-critical system.

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.

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 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.

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).

With the inception of model-based design and automatic code generation, many organizations are developing controls and diagnostics algorithms in model-based development tools to meet customer and regulatory requirements. Advances in model-based design have made it easier to generate C code from models and help software engineers streamline their workflow. Typically, after the software has been developed, the models are handed over to a calibration team responsible for calibrating the features to meet specified customer and regulatory requirements. However, once the models are handed over to the calibration team, the calibration engineers are unaware of how to calibrate the features because documentation is not available. Typically, model documentation trails behind the software process because it is created manually, most of this time is spent on formatting. As a result, lack of model documentation or up-to date documentation causes a lot of pain for OEM’s and Tier 1 suppliers.

A crash of a medium duty truck led to a study of the failure mechanism of the truck’s steering system. The truck, after being involved in a multi-vehicle vehicle collision, was found with its steering input shaft disconnected from the steering gear. The question arose whether the steering gear failure was a result of the collision, or causative to the collision. An in-depth investigation was conducted into whether forces on the vehicle due to the collision could cause the steering shaft to separate from the steering gear. Additionally, the performance of the steering gear with the adjuster nut progressively backed off was studied to determine the feedback a driver would receive if the steering gear came progressively apart. From the results of these studies, conclusions with regard to the crash causation were reached.

Lane Departure Warning (LDW) systems, along with other types of Advanced Driver Assistance Systems (ADAS), are becoming more common in passenger vehicles, with the general aim of improving driver safety through automation of various aspects of the driving task. Drivers have generally reported satisfaction with ADAS with the exception of LDW systems, which are often rated poorly or even deactivated by drivers. One potential contributor to this negative response may be an increase in the cognitive load associated with lane-keeping when LDW is in use. The present study sought to examine the relationship between LDW, lane-keeping behavior, and concurrent cognitive load, as measured by performance on a secondary task. Participants drove a vehicle equipped with LDW in a demarcated lane on a closed-course test track with and without the LDW system in use over multiple sessions.

Because the great majority of All-Terrain Vehicles (ATVs) use a solid rear axle for improved off-road mobility, these vehicles typically transition from understeer to oversteer with increased cornering severity in tests customarily used by automobile manufacturers to measure steady-state vehicle handling properties. An oversteer handling response is contrary to the accepted norm for on-road passenger vehicles and, for this reason, has drawn scrutiny from numerous researchers. In this paper, an evaluation of ATV handling is presented in which 10 participants operated an ATV that was configured to have two different steady-state cornering characteristics. One configuration produced an approximately linear understeer response (labeled US) and the other configuration transitioned from understeer to oversteer (labeled US-OS) with increasing lateral acceleration in constant-radius turn tests conducted on a skid pad.

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.

Fires involving cars, trucks, and other highway vehicles are a common concern for emergency responders. In 2013 alone, there were approximately 188,000 highway vehicle fires. Fire Service personnel are accustomed to responding to conventional vehicle (i.e., internal combustion engine [ICE]) fires, and generally receive training on the hazards associated with those vehicles and their subsystems. However, in light of the recent proliferation of electric drive vehicles (EDVs), a key question for emergency responders is, “what is different with EDVs and what tactical adjustments are required when responding to EDV fires?” The overall goal of this research program was to develop the technical basis for best practices for emergency response procedures for EDV battery incidents, with consideration for suppression methods and agents, personal protective equipment (PPE), and clean-up/overhaul operations.