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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 automated vehicle (AV) industry continues to progress, it is important to establish the level of operational safety of these vehicles prior to and throughout their deployment on public roads. The Institute of Automated Mobility (IAM) has previously proposed a set of operational safety assessment (OSA) metrics which can be used to quantify the operational safety of vehicles. The OSA metrics provide a starting point to consistently quantify performance, but a framework to interpret the metrics measurements is needed to objectively quantify the overall operational safety for a vehicle in a given scenario. This work aims to present an approach to applying a calculation of the safety envelope component of the OSA metrics to rear-world collisions for use in such an assessment. In this paper, the OSA methodology concept is introduced as a means for quantifying the operational safety of a vehicle.

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.

The operational safety of automated driving system (ADS)-equipped vehicles (AVs) must be quantified using well-defined metrics in order to gain an unambiguous understanding of the level of risk associated with AV deployment on public roads. In this research, efforts to evaluate the operational safety assessment (OSA) metrics introduced in prior work by the Institute of Automated Mobility (IAM) are described. An initial validation of the proposed set of OSA metrics involved using the open-source simulation software Car Learning to Act (CARLA) and Scenario Runner, which are used to place a subject vehicle in selected scenarios and obtain measurements for the various relevant OSA metrics. Car following scenarios were selected from the list of 37 pre-crash scenarios identified by the National Highway Traffic Safety Administration (NHTSA) as the most common driving situations that lead to crash events involving two light vehicles.

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.

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 vehicle and occupant responses in six vehicle-to-vehicle high-speed rear impact crash tests conducted at the Exponent Test and Engineering Center. The struck vehicle delta Vs ranged from 32 to 76 km/h and the vehicle centerline offsets varied from 5.7 to 114 cm. Five of the six tests were conducted with Hybrid III ATDs (Anthropometric Test Device) with two tests using the 50th male belted in the driver seat, one test with an unbelted 50th male in the driver seat, one test with a 95th male belted in the driver seat, and one with the 5th female lap belted in the left rear seat. All tests included vehicle instrumentation and three tests included ATD instrumentation. The ATD responses were analyzed and compared to corresponding IARVs (injury assessment reference values). Ground-based and onboard vehicle videos were synchronized with the vehicle kinematic data and biomechanical responses.

The purpose of this research is to document representative examples of control inputs and body positioning experienced riders use to control a motorcycle through maneuvers representative of those encountered during real-world operation. There is limited publicly available data that tracks the magnitude or direction of steering head rotation, steering torque input, etc. used by a rider to initiate and exit a turn as well as maintaining directional control during maneuvers ranging from slow parking lot turns to high speed lane changes. Using Exponent’s Test and Engineering Center (TEC) track and skid pad, a course was defined that included several maneuvers at various speeds and radii. A previous paper [1] investigated the influence of rider kinematics (weight shift) on motorcycle control.

In recent years, the discussion around the advent of highly automated vehicles has shifted from “if” to “when.” Commercially available vehicles already incorporate automated vehicle (AV) technologies of varying capability, and the eventual transition to fully automated systems, at least within certain predefined Operational Design Domains, is largely considered inevitable. While the full ramifications of this shift and the eventual depreciation of human driver control are still under intense debate, there is broad agreement on one issue -the advent of driverless systems will remove several constraints on the design of vehicle interior spaces, creating the opportunity for innovation. Even at this early stage, ambitious design concepts of purpose specific vehicles - mobile gyms, offices, bedrooms - have been proposed. More grounded designs, such as rotating passenger seats, have also been put forward.

Rear-end impacts are the most common crash scenario in the United States. Although automated vehicle (AV) technologies, such as frontal crash warning (FCW) and automatic emergency braking (AEB), are mitigating and preventing rear-end impacts, the technology is only gradually being introduced and currently has only limited effectiveness. Accordingly, there is a need to evaluate the current state of passive safety technologies, including the performance of seatbacks and head restraints. The objective of this study was to examine trends in head and neck loading during rear impact testing in new vehicle models over the prior decade. Data from 601 simulated rear impact sled tests (model years 2004 to 2018) conducted as a part of the Insurance Institute for Highway Safety (IIHS) Vehicle Seat/Head Restraint Evaluation Protocol were obtained.

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).

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.

Motorcycle stability during a variety of maneuvers is maintained through both rider steering input and body interactions with the seat, tank, footrests, and handlebars. Exploring how rider-vehicle interactions impact vehicle control is critical to creating a comprehensive understanding of motorcycle handling. The present study aims to understand how experienced motorcycle riders influence motorcycle dynamics by characterizing center of pressure (COP) location, force applied at the seat, rider lean angle and offset relative to the motorcycle, and steering angle for various maneuvers. A course was defined on Exponent’s Test and Engineering Center (TEC) track and skid pad that included sections of straight riding, navigating a banked curve, and sharp turning (low speed U-turns, 90 degree turn after a stop, and obstacle avoidance). The task influenced rider response and, in particular, lateral COP location at the seat.

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.

Unintended acceleration events due to pedal misapplication have been shown to occur more frequently in older vs. younger drivers. While such occurrences are well documented, the nature of these movement errors is not well-characterized in common pedal error scenarios: namely, on-road, non-emergency stopping or slowing maneuvers. It is commonly assumed that drivers move in a ballistic or “direct hit” trajectory from the accelerator to the brake pedal. However, recent simulator studies show that drivers do not always move directly between pedals, with older drivers displaying more variable foot trajectories than younger drivers. Our study investigated pedal movement trajectories in older drivers ages 67.9 ± 5.2 years (7 males, 8 females) during on-road driving in response to variable traffic light conditions. Three different sedans and a pick-up truck were utilized.

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.