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Effectively determining automated driving system (ADS)-equipped vehicle (AV) safety without relying on testing an infeasibly large number of driving scenarios is a challenge with wide recognition in industry and academia. The following paper builds on previous work by the Institute of Automated Mobility (IAM) and Science Foundation Arizona (SFAz), and proposes a test selection and scoring methodology (TSSM) as part of a safety case-based framework being developed by the SFAz to ensure the safety of AVs while addressing the scenario testing challenge. The TSSM is an AV verification and validation (V&V) process that relies, in part, on iterative, partially random generation of AV driving scenarios. These scenarios are generated using an operational design domain (ODD) and behavioral competency portfolio, which expresses the vehicle ODD and behavioral competencies in terms of quantifiable amounts or intensities of discrete components.

Studies of rollover accidents have reported crash attributes such as the number of rolls, rollout distance, initial over-the-ground speed, average roll rate, average over-the-ground deceleration, magnitude of roof deformation, cumulative damage, time and post-crash headroom. While these more general attributes are related to the repeated vehicle-to-ground impacts during a rollover, it has been previously shown [1] that a specific ground impact during a rollover and its consequences can be studied in more detail by using its acceleration time history (crash pulse or impulse) and energy loss. These two quantities are particularly meaningful to use when studying impact mechanics, however, they are limited to circumstances where the data exists, which means real-world on-road crashes cannot be used directly. Acceleration and energy data have been collected and previously published for three Subaru Forester dolly rollover tests, and have been studied in more detail in this writing.

There is a limited amount of data currently available on the acceleration and braking performances of school buses. This paper analyzes the braking performance of various Type A and Type C school buses with hydraulic and air brakes. The effect of ABS and Non-ABS systems as well as driver experience is discussed. A comparison with passenger car braking performance is presented. The acceleration of a school bus is also presented. Evaluations of “normal” and “rapid” accelerations are presented for Type A and Type B buses. A comparison with commonly used acceleration values for various vehicles is presented.

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.

Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) are considered as a promising future solution for sustainable transportation. This is due to the reduction in energy consumption when compared to conventional internal combustion engine (ICE) based vehicles. EVs and PHEVs contain an Energy Storage Systems (ESS). This increases the complexity of the system but also provides additional margins and fields for optimization. One of the most important elements of these vehicles is the ESS. The electrochemistry nature of battery systems is inherently sensitive to the temperature shifts. The shifts are controlled by the thermal management system of the traction battery systems, for electric-drive vehicles, which directly affects the overall vehicle dynamics. These dynamics include performance, long-term durability, and cost of the battery systems. Hence, thermal management becomes an essential element in the achievement to meet the demand for better performance.

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.

This paper explores tire placement with given tread depths on vehicles from two distinct perspectives. The first area explored is an analysis of crash data recently reported by the National Highway Traffic Safety Administration (NHTSA). In this report, thousands of tire-related crashes were investigated where the tread depth and inflation pressure were logged for each tire and assessments were made as to whether tire condition was a factor in the crash. The analysis of the data shows that in regards to accident causation, it is not statistically significant which axle has the deepest tread. What is significant is that a tread depth at or below 4/32″ anywhere on the vehicle leads to an increased rate of crashes. To understand the physics implied by the NHTSA data, a study was performed on how the placement of tires of various tread depths affects the steering, handling, and braking performance of a modern sport utility vehicle.

Colorimetric-solid phase extraction (C-SPE) is being developed as a method for in-flight monitoring of spacecraft water quality. C-SPE is based on measuring the change in the diffuse reflectance spectrum of indicator disks following exposure to a water sample. Previous microgravity testing has shown that air bubbles suspended in water samples can cause uncertainty in the volume of liquid passed through the disks, leading to errors in the determination of water quality parameter concentrations. We report here the results of a recent series of C-9 microgravity experiments designed to evaluate manual manipulation as a means to collect bubble-free water samples of specified volumes from water sample bags containing up to 47% air. The effectiveness of manual manipulation was verified by comparing the results from C-SPE analyses of silver(I) and iodine performed in-flight using samples collected and debubbled in microgravity to those performed on-ground using bubble-free samples.

The operational safety of Automated Driving System (ADS)-Operated Vehicles (AVs) are a rising concern with the deployment of AVs as prototypes being tested and also in commercial deployment. The robustness of safety evaluation systems is essential in determining the operational safety of AVs as they interact with human-driven vehicles. Extending upon earlier works of the Institute of Automated Mobility (IAM) that have explored the Operational Safety Assessment (OSA) metrics and infrastructure-based safety monitoring systems, in this work, we compare the performance of an infrastructure-based Light Detection And Ranging (LIDAR) system to an onboard vehicle-based LIDAR system in testing at the Maricopa County Department of Transportation SMARTDrive testbed in Anthem, Arizona. The sensor modalities are located in infrastructure and onboard the test vehicles, including LIDAR, cameras, a real-time differential GPS, and a drone with a camera.

With the development of vehicles equipped with automated driving systems, the need for systematic evaluation of AV performance has grown increasingly imperative. According to ISO 34502, one of the safety test objectives is to learn the minimum performance levels required for diverse scenarios. To address this need, this paper combines two essential methodologies - scenario-based testing procedures and scoring systems - to systematically evaluate the behavioral competence of AVs. In this study, we conduct comprehensive testing across diverse scenarios within a simulator environment following Mcity AV Driver Licensing Test procedure. These scenarios span several common real-world driving situations, including BV Cut-in, BV Lane Departure into VUT Path from Opposite Direction, BV Left Turn Across VUT Path, and BV Right Turn into VUT Path scenarios.

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.

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.

Over the past decade, significant progress has been made in developing algorithms and improving hardware for automated driving. However, conducting research and deploying advanced algorithms on automated vehicles for testing and validation remains costly, especially for academia. This paper presents the efforts of our research team to integrate the newest version of the open-source Autoware software with the commercially available DataSpeed Drive-by-Wire (DBW) system, resulting in the creation of a versatile and robust automated vehicle research platform. Autoware, an open-source software stack based on the 2nd generation Robot Operating System (ROS2), has gained prominence in the automated vehicle research community for its comprehensive suite of perception, planning, and control modules. The DataSpeed DBW system directly communicates with the vehicle's CAN bus and provides precise vehicle control capabilities.

Advanced Driver Assistive System (ADAS) technologies have been introduced as the automotive industry moves towards autonomous driving. One ADAS technology with the potential for substantial safety benefits is forward collision warning and mitigation (FCWM), which is designed to warn drivers of imminent front-end collisions, potentiate driver braking responses, and apply the vehicle's brakes autonomously. Although the proliferation of FCWM technologies can, in many ways, mitigate the necessity of a timely braking response by a driver in an emergency situation, how these systems affect a driver's overall ability to safely, efficiently, and comfortably operate a motor vehicle remains unclear. Exponent conducted a closed-course evaluation of drivers' reactions to an imminent forward collision event while driving an FCWM-equipped vehicle, either with or without a secondary task administered through a hands-free cell phone.

The driving safety performance of automated driving system (ADS)-equipped vehicles (AVs) must be quantified using metrics in order to be able to assess the driving safety performance and compare it to that of human-driven vehicles. In this research, driving safety performance metrics and methods for the measurement and analysis of said metrics are defined and/or developed. A comprehensive literature review of metrics that have been proposed for measuring the driving safety performance of both human-driven vehicles and AVs was conducted. A list of proposed metrics, including novel contributions to the literature, that collectively, quantitatively describe the driving safety performance of an AV was then compiled, including proximal surrogate indicators, driving behaviors, and rules-of-the-road violations.

There are established federal requirements and industry standards for frontal crash testing of motor vehicles. Consistently applied methods support reliability, repeatability, and comparability of performance metrics between tests and platforms. However, real world collisions are rarely identical to standard test protocols. This study examined the effects of occupant anthropometry and passive restraint deployment timing on occupant kinematics and biomechanical loading in a moderate-severity (approximately 30 kph delta-V) offset frontal crash scenario. An offset, front-to-rear vehicle-to-vehicle crash test was performed, and the dynamics of the vehicle experiencing the frontal collision were replicated in a series of three sled tests. Crash test and sled test vehicle kinematics were comparable. A standard or reduced-weight 50th percentile male Hybrid III ATD (H3-50M) or a standard 5th percentile female Hybrid III ATD (H3-5F) was belted in the driver’s seating position.

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.

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.

The operational safety of Automated Driving System-equipped vehicles (AVs) is a critical issue with AVs being deployed on public roads. Methodologies for evaluating the operational safety are therefore necessary to maintain public safety. One possible approach is a safety case established by the AV developer that uses evidence to support a structured argument that the AV exhibits a given level of operational safety. One of the key components of a safety case for AVs is a set of testing results showing behavioral competency in a variety of scenarios within the AV’s operational design domain (ODD). The Institute of Automated Mobility (IAM) has previously published operational safety assessment (OSA) metrics along with a means to evaluate the severity of violations of the safety envelope-type OSA metrics for navigation of individual scenarios in the proposed OSA Methodology.

Ensuring the safety of vulnerable road users (VRUs) such as pedestrians, users of micro-mobility vehicles, and cyclists is imperative for the commercialization of automated vehicles (AVs) in urban traffic scenarios. City traffic intersections are of particular concern due to the precarious situations VRUs often encounter when navigating these locations, primarily because of the unpredictable nature of urban traffic. Earlier work from the Institute of Automated Vehicles (IAM) has developed and evaluated Driving Assessment (DA) metrics for analyzing car following scenarios. In this work, we extend those evaluations to an urban traffic intersection testbed located in downtown Tempe, Arizona. A multimodal infrastructure sensor setup, comprising a high-density, 128-channel LiDAR and a 720p RGB camera, was employed to collect data during the dusk period, with the objective of capturing data during the transition from daylight to night.