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Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration and speed, with each other. Using sensors such as radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, a CACC system is presented, where the acceleration of the lead vehicle is used in the calculation of desired vehicle speed. In addition to the smooth car following abilities, the proposed CACC also has the capability to calculate a speed profile for the ego vehicle that is fuel efficient, making it an Ecological CACC (Eco-CACC) model.

Due to the lack of biofidelity seen in GHBMC M50-O in rear-facing impact simulations involving interaction with the seat back in an OEM seat, it is important to explore how the boundary conditions might be affecting the biofidelity and potentially formulate methods to improve biofidelity of different occupant models in the future while also maintaining seat validity. This study investigated the influence of one such boundary condition, which is the seat back foam material properties, on the thorax and pelvis kinematics and injury outcomes of the GHBMC 50th M50-O model in a high-speed rear-facing frontal impact scenario, which involves severe occupant loading of the seat back. Two different seat back foam materials were used – a stiff foam with high densification and a soft foam with low densification. The peak magnitudes of the T-spine resultant accelerations of the GHBMC M50-O increased with the use of soft foam as compared to stiff foam.

NASA’s Watts on the Moon Challenge is seeking solutions to transfer at least 1.065 kW power from a 120 V dc source to a 24-32 V dc load over a 3-km distance under the same environmental conditions as the Lunar surface (i.e., 77 K temperature and 1 mTorr pressure). The selected solution from the author’s team proposed utilizing two modular multilevel Gallium Nitride (GaN) based isolated dc-dc converters to connect the 120 V dc source with the 24-32 V dc load bank via 1.5 kV rated dc transmission lines. The modular multilevel converters feature frequency multiplication, high step-down voltage ratio and low device voltage stress. In the converters, GaN gate injection transistor (GaN GIT) and GaN High-Electron-Mobility Transistor (GaN HEMT) devices are chosen as switching devices, due to the merits of lower power loss, radiation hardness and ability to work under cryogenic and vacuum conditions.

Microgrids are a topic of interest in recent years, largely due to their compatibility with the integration of distributed renewable resources, capability for bidirectional power flow, and ability to reconfigure to mitigate the effects of faults. Fault diagnosis algorithms are a foundational technology for microgrids. These algorithms must have two primary capabilities. First, faults must be detectable; it is known when the fault occurs. Second, faults must be isolable; the type and location of detected faults can be determined. However, most fault handling research considering microgrids has focused on the protection algorithm. Protection algorithms seek to quickly extinguish dangerous faults which can damage components. However, these algorithms may not sufficiently capture less severe faults, or provide comprehensive monitoring for the microgrid. This is particularly relevant when considering applications involving fault tolerant control or dynamic grid reconfiguration.

This paper proposes the use of an on-demand, ride hailed and ride-Shared Autonomous Vehicle (SAV) service as a feasible solution to serve the mobility needs of a small city where fixed route, circulator type public transportation may be too expensive to operate. The presented work builds upon our earlier work that modeled the city of Marysville, Ohio as an example of such a city, with realistic traffic behavior, and trip requests. A simple SAV dispatcher is implemented to model the behavior of the proposed on-demand mobility service. The goal of the service is to optimally distribute SAVs along the network to allocate passengers and shared rides. The pickup and drop-off locations are strategically placed along the network to provide mobility from affordable housing, which are also transit deserts, to locations corresponding to jobs and other opportunities.

Testing vision-based advanced driver assistance systems (ADAS) in a Camera-in-the-Loop (CiL) bench setup, where external visual inputs are used to stimulate the system, provides an opportunity to experiment with a wide variety of test scenarios, different types of vehicle actors, vulnerable road users, and weather conditions that may be difficult to replicate in the real world. In addition, once the CiL bench is setup and operating, experiments can be performed in less time when compared to track testing alternatives. In order to better quantify normal operating zones, track testing results were used to identify behavior corridors via a statistical methodology. After determining normal operational variability via track testing of baseline stationary surrogate vehicle and pedestrian scenarios, these operating zones were applied to screen-based testing in a CiL test setup to determine particularly challenging scenarios which might benefit from replication in a track testing environment.

1 Autonomous Driving Systems (ADS) are developing rapidly. As vehicle technology advances to SAE level 3 and above (L4, L5), there is a need to maximize and verify safety and operational benefits. As a result, maintenance of these ADS systems is essential which includes scheduled, condition-based, risk-based, and predictive maintenance. A lot of techniques and methods have been developed and are being used in the maintenance of conventional vehicles as well as other industries, but ADS is new technology and several of these maintenance types are still being developed as well as adapted for ADS. In this work, we are presenting a systematic literature review of the “State of the Art” knowledge for the maintenance of a fleet of ADS which includes fault diagnostics, prognostics, predictive maintenance, and preventive maintenance.

The National Highway Traffic Safety Administration (NHTSA) has developed the Large Omnidirectional Child (LODC) Anthropomorphic Test Device (ATD) to improve the biofidelity of the currently available Hybrid III 10-year-old (HIII-10C) ATD. The improvements of the LODC over the HIII-10C include changes in sub-assemblies such as the head and neck, where the LODC head is a redesigned HIII-10C head with pediatric mass properties and the neck has a modified atlanto-occipital joint to replicate observations made from human specimens. The current study focuses on developing a dynamic, nonlinear finite element (FE) model of the LODC ATD head and neck complex. The FE mesh is generated using HyperMesh based on the three-dimensional CAD model. The material data, contact definitions and initial conditions are defined in LS-PrePost and converted to LS-Dyna solver input format. The initial and boundary conditions are defined to replicate the neck flexion experimental tests.

In order to validate the operation of advanced driver assistance systems (ADAS), tests must be performed that assess the performance of the system in response to different scenarios. Some of these systems are designed for crash-imminent situations, and safely testing them requires large stretches of controlled pavement, expensive surrogate targets, and a fully functional vehicle. As a possible more-manageable alternative to testing the full vehicle in these situations, this study sought to explore whether these systems could be isolated, and tests could be performed on a bench via a hardware-in-the-loop methodology. For camera systems, these benches are called Camera-in-the-Loop (CiL) systems and involve presenting visual stimuli to the device via an external input.

The lack of inherent security controls makes traditional Controller Area Network (CAN) buses vulnerable to Machine-In-The-Middle (MitM) cybersecurity attacks. Conventional vehicular MitM attacks involve tampering with the hardware to directly manipulate CAN bus traffic. We show, however, that MitM attacks can be realized without direct tampering of any CAN hardware. Our demonstration leverages how diagnostic applications based on RP1210 are vulnerable to Machine-In-The-Middle attacks. Test results show SAE J1939 communications, including single frame and multi-framed broadcast and on-request messages, are susceptible to data manipulation attacks where a shim DLL is used as a Machine-In-The-Middle. The demonstration shows these attacks can manipulate data that may mislead vehicle operators into taking the wrong actions.

Child occupants have not been studied in far-side impacts as thoroughly as frontal or near side crash modes. The objective is to determine whether the installation method of child restraint systems (CRS) affects far-side crash performance. Twenty far-side impact sled tests were conducted with rear-facing (RF) CRS, forward-facing (FF) CRS, high-back boosters, and belt only. Each was installed on second row captain’s chairs from a recent model year minivan. Common CRS installation errors were tested, including using the seat belt in Emergency Locking Mode (ELR) instead of Automatic Locking Mode (ALR), not attaching the top tether, and using both the lower anchors (LA) and seat belt together. Correct installations were also tested as a baseline comparison. Q3s and Hybrid III 6-year-old (6yo) anthropomorphic test devices (ATDs) were used. Lateral displacements of the CRS and head were examined as well as injury metrics in the head, spine, and torso.

Many vehicles allow consumers to adapt the vehicle environment to their families’ needs by folding or removing one or more rear row seats. It is currently unclear how different seat configurations affect child restraint systems (CRS) installed in adjacent seats. The objective is to quantify CRS performance in far-side impacts when the seating position adjacent to the CRS is in its normal upright position, folded in half, or removed. Twelve tests were conducted. Second row seats from a recent model year minivan were obtained, including full size captain’s chairs from the outboard positions and narrow seats from the center position. Rear-facing (RF) and forward-facing (FF) CRS were installed one at a time in either the outboard or center position. The seating position adjacent to the CRS was set in either the standard upright position, folded in half, or removed. Far-side impacts were conducted at 10° anterior of pure lateral at 24.8 ± 0.2 g. The Q3s ATD was used for all tests.

Teammate Advanced Drive is a driving support system with state-of-the-art automated driving technology that has been developed for customers’ safe and secure driving on highways based on the Toyota’s Mobility Teammate Concept. This SAE Level 2 (L2) system assists overtaking, lane changes, and branching to the destination, in addition to providing hands-free lane centering and car following. The automated driving technology includes self-localization onto a High Definition Map, multi-modal sensing to cover 360 degrees of the surrounding environment using fusion of LiDARs, cameras, and radars, and a redundant architecture to realize fail-safe operation when a malfunction or system limitation occurs. High-performance computing is provided to implement deep learning for predicting and responding to various situations that may be encountered while driving.

Testing any new safety technology of Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) requires simulation-based validation and verification. The specific scenarios used for testing, outline incidences of accidents or near-miss events. In order to simulate these scenarios, specific values for all the above parameters are required including the ego vehicle model. The ‘criticality’ of a scenario is defined in terms of the difficulty level of the safety maneuver. A scenario could be over-critical, critical, or under-critical. In over-critical scenarios, it is impossible to avoid a crash whereas, for under-critical scenarios, no action may be required to avoid a crash. The criticality of the scenario depends on various parameters e.g. speeds, distances, road/tire parameters, etc. In this paper, we propose a definition of criticality metric and identify the parameters such that a scenario becomes critical.

Collisions between opened doors and approaching vehicles such as bicycles are common occurrences in urban areas around the world. For example, in Chicago, 20% of all bicycle accidents involve collisions with doors, which occur over 300 times a year. In addition, there are concerns about a further rise in accidents due to the recent increase in home delivery services and bicycle commuting during the COVID-19 pandemic. Some advanced driver assistance systems (ADAS) that are designed to help prevent this type of accident have already been introduced. These systems detect approaching vehicles with sensors and alert the person opening the door via LED lights or a buzzer when the door is opened. The occupant must understand the meaning of the alert and stop opening the door quickly to prevent an accident. However, if the occupant is an elderly person or a child, it is difficult to stop opening the door quickly.

This research leverages publicly available crash data to construct safety-critical scenarios focusing primarily on Level 3 Automated Driving Systems (ADS) safety assessment under highway driving conditions. NHTSA’s Crashworthiness Data System (CDS) has a rich dataset of representative crashes sampled from numerous Primary Sampling Units (PSUs) across the country. Each of these datasets includes the storyline, road geometry information, detailed description of actors involved in the crash, weather information, scene diagrams, crash images, and a myriad of other crash-specific details. The methodology adopted aims to generate critical scenarios from real-world driving to complement the existent regulatory tests for the validation of L3 ADS. For this work, a four-step approach was adopted to extract safety-critical scenarios from crash data.

This paper describes a method for assisting pedestrians to cross a road. As motorization develops, pedestrian protection techniques are becoming more and more important. Advanced driving assistance systems (ADAS) are improving rapidly to provide even greater safety. However, since the accident risk of pedestrians remains high, the development of an advanced walking assistance system for pedestrian protection may be an effective means of reducing pedestrian accidents. Crossing a road is one of the highest risk events, and is a complex phenomenon that consists of many dynamically changing elements such as vehicles, traffic signals, bicycles, and the like. A road crossing assistance system requires three items: real-time situational recognition, a robust decision-making function, and reliable information transmission. Edge devices equipped with autonomous systems are one means of achieving these requirements.

Vulnerable Road User (VRU) safety has been an important issue throughout the years as corresponding fatality numbers in traffic have been increasing each year. With the developments in connected vehicle technology, there are new and easier ways of implementing Vehicle to Everything (V2X) communication which can be utilized to provide safety and early warning benefits for VRUs. Mobile phones are one important point of interest with their sensors being increased in quantity and quality and improved in terms of accuracy. Bluetooth and extended Bluetooth technology in mobile phones has enhanced support to carry larger chunks of information to longer distances. The work we discuss in this paper is related to a mobile application that utilizes the mobile phone sensors and Bluetooth communication to implement Personal Safety Message (PSM) broadcast using the SAE J2735 standard to create a Pedestrian to Vehicle (P2V) based safety warning structure.

Electric transportation has the potential of mitigating CO2 emissions and reduce fuel needs. One of the challenges for the growth of this industry is limited range and efficiency of the vehicles associated with battery storage systems and electric drive technology. High voltage systems are expected to increase efficiency and then vehicle mileage, however this increases the severity of the fault conditions, especially in case of short circuit. Melting fuse is commonly used for the purpose of protection in electrified vehicles, while it is effective and reliable, there are several shortcomings such as lack of precision, effect of ambient temperature, bulky, interruption time depending on the fault condition etc. Additionally, the on-board DC power distribution system (PDS) is characterized by low impedance, in this environment fuses are not able to limit the fault current leading to damage of electronics and hazard for the battery pack.

The new Toyota Mirai hydrogen fuel cell electric vehicle (FCEV) was launched in December 2020. Achieving a low-cost, high-performance FC stack is an important objective in FCEV development. At the same time, it is also necessary to ensure vehicle safety. This paper presents an overview of the safety requirements for onboard FC stacks. It also describes the simulation and evaluation methods for the following matters related to the FC stack. i) Impact force resistance: The FC stack was designed to prevent cell layer slippage due to impact. Constraint force between the cell layers is provided by the frictional force between the cells and an external constraint. A simulation of the behavior of the cell layers under impact force was developed. The impact force resistance was confirmed by an impact loading test. ii) Hydrogen safety: The FC stack was designed so that permeated hydrogen is ventilated and the hydrogen concentration is kept below the standard.