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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.

The design and development of new biologically inspired technologies based on intelligent materials that are capable of sensing the levels of target biomolecules and, if needed, trigger appropriate countermeasures to regulate biological processes and rhythms of the astronauts is being undertaken in our laboratories. This is accomplished by coupling biologically inspired sensors that monitor the levels of the target biomolecules with intelligent polymeric materials that can regulate the release of a countermeasure. The technology developed here integrates sensors and artificial muscle material into a self-regulating device that can perform with minimal crew intervention. Further, it takes advantage of microfabrication technology to construct lightweight and robust responsive delivery systems. These “intelligent” devices address the need for the control and regulation of biological processes and rhythms under spaceflight conditions.

Automobile manufacturers are reducing the weight of their vehicles in order to meet strict fuel economy legislation. To achieve this goal, a combination of different materials such as steel, aluminum and carbon fiber composites are being considered for use in vehicle bodies. The ability to join these different materials is an ongoing challenge and an area of research for automobile manufacturers. Multiridged fasteners are a viable option for this type of multi-material joining. Commercial systems exist and are being used in the industry, however, new ridged nail designs offer the potential for improvement in several areas. The goal of this paper is to prototype and test a safer flat-end fastener whilst not compromising on strength characteristics, to prevent injury to factory workers. The nails were prototyped using existing RIVTAC® nails.

Many smart cities and car manufacturers have been investing in Vehicle to Infrastructure (V2I) applications by integrating the Dedicated Short-Range Communication (DSRC) technology to improve the fuel economy, safety, and ride comfort for the end users. For example, Columbus, OH, USA is placing DSRC Road Side Units (RSU) to the traffic lights which will publish traffic light Signal Phase and Timing (SPaT) information. With DSRC On Board Unit (OBU) equipped vehicles, people will start benefiting from this technology. In this paper, to accelerate the V2I application development for Connected and Autonomous Vehicles (CAV), a Hardware in the Loop (HIL) simulator with DSRC RSU and OBU is presented. The developed HIL simulator environment is employed to implement, develop and evaluate V2I connected vehicle applications in a fast, safe and cost-effective manner.

The rise of automation in the automotive industry has ensured significant progress in vehicle safety and infrastructure. During the transition to full autonomy, the driver is often the redundancy and safety feature in the event of a hazard or automation error. Understanding driver behavior in the transition from non-driver to driver is important for safety. Proper handling of transitions will be more critical as these events become less common and users trust automated driving systems. This research investigates the case of SAE level-3 automated driving systems, where the driver need not constantly pay attention but is responsible for reaction during hazards. Findings include quantitative and qualitative assessment of various warning modes for a distracted driver responding to an automated driving failure situation. Driver response time and behavior for these events are compared to instances with minimal warning systems.

Global regulatory targets and customer demand are driving the automotive industry to improve vehicle fuel efficiency. Methods for achieving increased efficiency include improvements in the internal combustion engine and an accelerating shift toward electrification. A key enabler to maximizing the benefit from these new powertrain technologies is proper systems integration work - including developing optimized controls for the propulsion system as a whole. The next step in the evolution of improving the propulsion management system is to make use of available information not typically associated with the powertrain. Advanced driver assistance systems, vehicle connectivity systems and cloud applications can provide information to the propulsion management system that allows a shift from instantaneous optimization of fuel consumption, to optimization over a route. In the current paper, we present initial work from a project being done as part of the DOE ARPA-E NEXTCAR program.

The EcoCAR 2: Plugging into the Future team at The Ohio State University is designing a Parallel-Series Plug-in Hybrid Electric Vehicle capable of 44 miles of all-electric range. The vehicle features an 18.9-kWh lithium-ion battery pack with range extending operation in both series and parallel modes. This is made possible by a 1.8-L ethanol (E85) engine and 6-speed automated manual transmission. This vehicle is designed to drastically reduce fuel consumption, with a utility factor weighted fuel economy of 50 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This paper details three years of modeling and simulation development for the OSU EcoCAR 2 vehicle. Included in this paper are the processes for developing simulation platform and model requirements, plant model and soft ECU development, test development and validation, automated regression testing, and controls and calibration optimization.

With the advancement of the autonomous vehicle development, the different possibilities of improving fuel economy have increased significantly by changing the driver or powertrain response under different traffic conditions. Development of new fuel-efficient driving strategies requires extensive experiments and simulations in traffic. In this paper, a fuel efficiency simulator environment with existing simulator software such as Simulink, Vissim, Sumo, and CarSim is developed in order to reduce the overall effort required for developing new fuel-efficient algorithms. The simulation environment is created by combining a mid-sized sedan MATLAB-Simulink powertrain model with a realistic microscopic traffic simulation program. To simulate the traffic realistically, real roads from urban and highway sections are modeled in the simulator with different traffic densities.

In the past, research on blockchain technology has addressed security and privacy concerns within intelligent transportation systems for critical V2I and V2V communications that form the backbone of Internet of Vehicles. Within trucking industry, a recent trend has been observed towards the use of blockchain technology for operations. Industry stakeholders are particularly looking forward to refining status quo contract management and vehicle maintenance processes through blockchains. However, the use of blockchain technology for enhancing vehicle performance in fleets, especially while considering the fact that modern-day intelligent vehicles are prone to cyber security threats, is an area that has attracted less attention. In this paper, we demonstrate a case study that makes use of blockchains to securely optimize the fuel economy of fleets that do package pickup and delivery (P&D) in urban areas.

By utilizing the vehicle to infrastructure communication, the conventional Green Light Optimized Speed Advisory (GLOSA) applications give speed advisory range for drivers to travel to pass at the green light. However, these systems do not consider the traffic between the ego vehicle and the traffic light location, resulting in inaccurate speed advisories. Therefore, the driver needs to intuitively adjust the vehicle's speed to pass at the green light and avoid traffic in these scenarios. Furthermore, inaccurate speed advisories may result in unnecessary acceleration and deceleration, resulting in poor fuel efficiency and comfort. To address these shortcomings of conventional GLOSA, in this study, we proposed the utilization of collaborative perception messages shared by smart infrastructures to create an enhanced speed advisory for the connected vehicle drivers and automated vehicles.

In the last decade, the accelerated advancements in manufacturing techniques and material science enabled the automotive industry to manufacture commercial vehicles at more affordable rates. This, however, brought about roadways having to accommodate an ever-increasing number of vehicles every day. However, some roadways, during specific hours of the day, had already been on the brink of reaching their capacity to withstand the number of vehicles travelling on them. Hence, overcrowded roadways create slow traffic, and sometimes, bottlenecks. In this paper, a Dynamic Speed Harmonization (DSH) algorithm that regulates the speed of a vehicle to prevent it from being affected by bottlenecks has been presented. First, co-simulations were run between MATLAB Simulink and CarSim to test different deceleration profiles.

In a conventional passenger vehicle, the AC system is the largest ancillary load. This paper proposes a novel control strategy to reduce the energy consumption of the air conditioning system of a conventional passenger car. The problem of reducing the parasitic load of the AC system is first approached as a multi-objective optimization problem. Starting from a validated control-oriented model of an automotive AC system, an optimization problem is formalized to achieve the best possible fuel economy over a regulatory driving cycle, while guaranteeing the passenger comfort in terms of cabin temperature and reduce the wear of the components. To complete the formulation of the problem, a set of constraints on the pressure in the heat exchanger are defined to guarantee the safe operation of the system. The Dynamic Programming (DP), a numerical optimization technique, is then used to obtain the optimal solution in form of a control sequence over a prescribed driving cycle.

Evaluating the safety of Autonomous Vehicles (AV) is a challenging problem, especially in traffic conditions involving dynamic interactions. A thorough evaluation of the vehicle’s decisions at all possible critical scenarios is necessary for estimating and validating its safety. However, predicting the response of the vehicle to dynamic traffic conditions can be the first step in the complex problem of understanding vehicle’s behavior. This predicted response of the vehicle can be used in validating vehicle’s safety. In this paper, models based on Machine Learning were explored for predicting and classifying driver’s response. The Naturalistic Driving Study dataset (NDS), which is part of the Strategic Highway Research Program-2 (SHRP2) was used for training and validating these Machine Learning models.

Electronic stability control (ESC) has been an essential part of road vehicle safety for almost three decades. In April of 2007, the United States federal government issued a regulation to test the validity of ESC in development vehicles, and the regulation is called Federal Motor Vehicle Safety Standards (FMVSS) 126 in North America (NA), and an equivalent test in other countries outside of NA called ECE13-H (Economic Commission for Europe). While these standards have been used to certify ESC in development passenger cars for over a decade, this has not yet been scrutinized for the application of autonomous vehicles. Autonomous cars have sensors and control systems which can be used to improve ESC, where commercial standard vehicles do not.

The power loss of bearings is a significant factor in the overall efficiency in a drive unit system. Such bearings are subject to combined radial and axial loading needed to support the gear mesh forces. An experimental methodology has been developed to perform sets of power loss measurements on TRB, 4PCBB and DGBB. These measurements were performed under a variety of speed, load, temperature, and lubrication conditions. The loss behaviors of these types of the bearings are discussed, along with the tradeoff of different bearing arrangements for the fuel economy cycles. Several power loss models are employed to assess the accuracy of the estimations as compared to the experimental measurements. At low speed some models showed good correlations for TRB and DGBB, while at higher speed, they start deviating from the testing results. A higher fidelity model for estimating the losses at high speed, especially speed around 20krpm and beyond, needs to be developed.

This study examines the performance of rear-facing child restraint systems (RF CRS) in moderate severity rear impact sled tests. The study also investigates the effects of RF CRS features on CRS kinematics and anthropomorphic test device (ATD) injury metrics in this scenario. Twelve tests were conducted at a moderate severity rear impact sled pulse (approximately 28.2 km/h and 18.4 g). Four models of RF CRS were tested in the rear outboard positions of a sedan seat. The CRABI 12-month-old and Hybrid III 3-year-old ATDs were instrumented with head and chest accelerometers, head angular rate sensors, six-axis upper neck load cells, and a chest linear potentiometer (3-year-old only). The effects of carry handle position, occupant size, presence of anti-rebound bar, Swedish style tethering, and lower anchor vs. seat belt installation were investigated. Data were also compared to pediatric injury assessment reference values (IARV).

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.

In the face of growing concerns about environmental sustainability and urban congestion, the integration of eco-driving strategies has emerged as a pivotal solution in the field of the urban transportation sector. This study explores the potential benefits of a CAV functioning as a virtual eco-driving controller in an urban traffic scenario with a group of following human-driven vehicles. A computationally inexpensive and realistic powertrain model and energy management system of the Chrysler Pacifica PHEV are developed with the field experiment data and integrated into a forward-looking vehicle simulator to implement and validate an eco-driving speed planning and energy management strategy assuming longitudinal automation. The eco-driving algorithm determines the optimal vehicle speed profile and energy management strategy.

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.