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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.

Deep learning based approaches for object detection are heavily dependent on the nature of data used for training, especially for vehicles driving in cluttered urban environments. Consequently, the performance of Convolutional Neural Network (CNN) architectures designed and trained using data captured under clear weather and favorable conditions, could degrade rather significantly when tested under cloudy and rainy conditions. This naturally becomes a major safety issue for emerging autonomous vehicle platforms relying on CNN based object detection methods. Furthermore, despite a noticeable progress in the development of advanced visual deraining algorithms, they still have inherent limitations for improving the performance of state-of-the-art object detection. In this paper, we address this problem area by make the following contributions.

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.

A multicomponent droplet evaporation model which discretizes the one-dimensional mass and temperature profiles inside a droplet with a finite volume method has been developed and implemented into a large-eddy simulation (LES) model for spray simulations. The LES and multicomponent models were used along with the KH-RT secondary droplet breakup model to simulate realistic fuel sprays in a closed vessel. The effect of various spray and ambient gas parameters on the liquid penetration length of different single component and multicomponent fuels was investigated. The numerical results indicate that the spray penetration length decreases non-linearly with increasing gas temperature or pressure and is less sensitive to changes in ambient gas conditions at higher temperatures or pressures. The spray models and LES were found to predict the experimental results for n-hexadecane and two multicomponent surrogate diesel fuels reasonably well.

The development of connected-vehicle technology, which includes vehicle-vehicle and vehicle-infrastructure communications, opens the door for unprecedented active safety and driver-enhanced systems. In addition to exchanging basic traffic messages among vehicles for safety applications, a significantly higher level of safety can be achieved when vehicles and designated infrastructure-locations share their sensor data. In this paper, we propose a new system where cameras installed on multiple vehicles and infrastructure-locations share and fuse their visual data and detected objects in real-time. The transmission of camera data and/or detected objects (e.g., pedestrians, vehicles, cyclists, etc.) can be accomplished by many communication methods. In particular, such communications can be accomplished using the emerging Dedicated Short-Range Communications (DSRC) technology.

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.

Driver and passenger comfort, as related to automotive seats, is a growing issue in the automotive industry. As this trend continues, automotive seat designers and developers are generating a greater need for more anthropometrically accurate tools to aid them in their work. One tool being developed is the JOHN software program that utilizes three-dimensional solid objects to represent humans in seated postures. Contours have been developed to represent the outside skin surfaces of three different body types in a variety of postures in the sagittal plane. These body types include: the small female, the average male, and the large male.

The subject of offtracking has been considered as a low speed phenomenon, amenable to analysis via small mechanical models or straightforward calculations. This paper views offtracking from a high speed as well as a low speed vantage point. A mathematical model with one degree of freedom is used to show that there is a speed, well within the routine driving range and independent of radius, at which there will be no offtracking in a steady turn. At higher speeds the trailer will track outside the steady turn circle, and at lower speeds the trailer will track inside the steady turn circle. The analysis indicates similar behavior in a lane change maneuver - small offtracking was found to occur at the steady turn zero-off tracking speed, and larger off tracking was found to occur at both higher and lower speeds.

LiDAR sensors are common in automated driving due to their high accuracy. However, LiDAR processing algorithm development suffers from lack of diverse training data, partly due to sensors’ high cost and rapid development cycles. Public datasets (e.g. KITTI) offer poor coverage of edge cases, whereas these samples are essential for safer self-driving. We address the unmet need for abundant, high-quality LiDAR data with the development of a synthetic LiDAR point cloud generation tool and validate this tool’s performance using the KITTI-trained PIXOR object detection model. The tool uses a single camera raycasting process and filtering techniques to generate segmented and annotated class specific datasets.

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.

Challenges that persons with disabilities face with current modes of transportation have led to difficulties in carrying out everyday tasks, such as grocery shopping and going to doctors’ appointments. Autonomous vehicles have been proposed as a solution to overcome these challenges and make these everyday tasks more accessible. For these vehicles to be fully accessible, the infrastructure surrounding them need to be safe, easy to use, and intuitive for people with disabilities. Thus, the goal of this work was to analyze interview data from persons with disabilities, and their caregivers, to identify barriers to accessibility for current modes of transportation and ways to ameliorate them in pick up/drop off zones for autonomous vehicles. To do this, interview subjects were recruited from adaptive sports clubs, assistive living facilities, and other disability networks to discuss challenges with current public transit stops/stations.

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.

To obtain the maximum output power and fuel economy from an internal combustion engine, it is often necessary to detect engine knock and operate the engine at its knock limit. This paper presents the ability to detect knock using in-cylinder ionization signals on a large displacement, air-cooled, “V” twin motorcycle engine over the engine operational map. The knock detection ability of three different sensors is compared: production knock (accelerometer) sensor, in-cylinder pressure sensor, and ionization sensor. The test data shows that the ionization sensor is able to detect knock better than the production knock sensor when there is high mechanical noise present in the engine.

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.

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.