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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.

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.

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.

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.

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.

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.

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.

Three years of data from the Large Truck Crash Causation Study (LTCCS) were analyzed to identify accidents involving heavy trucks (GVWR >10,000 lbs.). Risk of rollover and ejection was determined as well as belt usage rates. Risk of ejection was also analyzed based on rollover status and belt use. The Abbreviated Injury Scale (AIS) was used as an injury rating system for the involved vehicle occupants. These data were further analyzed to determine injury distribution based on factors such as crash type, ejection, and restraint system use. The maximum AIS score (MAIS) was analyzed and each body region (head, face, spine, thorax, abdomen, upper extremity, and lower extremity) was considered for an AIS score of three or greater (AIS 3+). The majority of heavy truck occupants in this study were belted (71%), only 2.5% of occupants were completely or partially ejected, and 28% experienced a rollover event.

Temperature measurements during a full-scale burn test of a 2001 full-size pickup truck showed that the fire progressed in distinct stages in both the engine and passenger compartments. Although the fire started in the engine compartment and had a relatively long growth period, when a localized area reached about 700°C, a distinct transition occurred where the rate of fire spread increased, leading to full involvement of all engine compartment combustibles. As the engine compartment became fully involved, a hot gas layer then accumulated at the ceiling of the passenger compartment, producing a strong vertical temperature gradient. When the temperature at the ceiling reached about 600°C, another distinct transition occurred where the rate of fire spread increased, leading to full involvement of the passenger compartment. The highest temperature during the test occurred within the engine compartment in an area that had the greatest fuel load, and not the area of origin.

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.

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.

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.

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.

Trailing axles, otherwise known as tag axles, are utilized in many states to allow heavy duty dump trucks and cement trucks to maximize their capacity. The trailing axle is an additional axle mounted on an arm on the rear of the truck that can be raised and lowered. When lowered, the axle extends the overall wheelbase of the vehicle and increases the total number of axles, thereby allowing for additional load to be carried without exceeding load-restriction regulations. There are multiple manufactures of trailing axles that utilize different suspension designs. One design uses an articulating axle that is mounted to the framework that lowers it. In this study, the sensitivity of this design to tire blowout on one of the trailing axle tires is studied. Testing was conducted that involved initiating a sudden air-loss event by creating a hole in the sidewall of the tire. The handling response of the vehicle was documented with on-board instrumentation and on-board and off-board video.

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.

The emergence of Plug-in hybrid electric vehicles (PHEVs) and electric vehicles (EVs) as a viable means of transportation has been coincident with the development of lithium-ion battery technology and electronics that have enabled the storage and use of large amounts of energy that were previously only possible with internal combustion engines. However, the safety aspects of using these large energy storage battery packs are a significant challenge to address. For example an unintentional sudden release of energy, such as through a thermal runaway event, is a common concern. Developing thermal management systems for upset conditions in battery packs requires a clear understanding of the heat generation mechanisms and kinetics associated with the failures of Li-ion batteries.

As lithium-ion cells and systems become larger and more ubiquitous in automotive applications, fire and explosion hazards that are rare or non-existent in smaller systems may exist in these larger systems. One potential hazard can occur when flammable gases emitted from a lithium-ion cell failure accumulate in or around automobiles and are ignited by electrical activity or by the cells themselves and result in a fire or explosion. In some instances, the safety aspects related to fires and explosions protection of electric vehicles and hybrid vehicles using these large energy storage battery packs are a significant challenge to address. This paper describes and characterizes the combustion and explosion hazards that can occur when a lithium ion battery pack fails and goes into thermal runaway in an enclosed space. Metrics such as gas composition, maximum overpressure, rate of pressure rise, and flammability limits are described.

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.

The emergence of Plug-in hybrid electric vehicles (PHEVs) and electric vehicles (EVs) as a viable means of transportation has been coincident with the development of lithium-ion (Li-ion) battery technology and electronics. These developments have enabled the storage and use of large amounts of energy that were previously only possible with internal combustion engines. However, the safety aspects of using these large energy storage battery packs are a significant challenge to address. In addition, the rapid advances in electrode and electrolyte materials for Li-Ion batteries have made comparisons and ranking of safety parameters difficult because of the substantial variations in cell designs. In this work, we outline a method for quantifying the thermal safety aspects of Li-ion battery technologies using a Cone Calorimeter.