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Digital mapping tools have become indispensable for road navigation. Applications like Waze and Google Maps harness the power of satellite imagery to provide precise visualization of GPS coordinates. The field advanced significantly in May 2023 with the introduction of dynamic 3D representations of the Earth. Companies such as Cesium now offer Unity3D and Unreal Engine Application Programming Interface that can be applied to geospatial applications. These images are no longer static and offer the opportunity to provide seamless continuous navigation. Driving simulation has been widely used for training and research. We investigate with this project the potential of this new geospatial database as a tool for scenario development to study manual and autonomous driving. We present an in-vehicle driving simulation integration that employs a real steering wheel and pedals from a stationary vehicle as controls.

With a view to promote mobility electrification, improved comfort and handling with lower cost are crucial factors in next generation of EV and HEV design. In contrast to ICE platform, electrified counterparts displays distinct NVH characteristics that present challenges in terms of weight transfer, steering, motor vibrations, etc. From a holistic perspective, this paper proposes a compounded suspension system serving dual purpose of dynamic damping and power rejuvenation utilizing electric motor as part of the tuned mass damper inertia system. A variable inertance mechanism is developed in form of geartrain while motor vibration itself receives calculated harness through tuned mass damping. Furthermore, suspension deformation undergoes desirable mitigation as a result of effective simulated annealing optimization focused on shifting objective value according to input tradeoff prediction. Nonlinear system dynamics are considered as a means to broaden the damping bandwidth.

As vehicles with SAE level 2 of autonomy become more widely deployed, they still rely on the human driver to monitor the driving task and take control during emergencies. It is therefore necessary to examine the Human Factors affecting a driver’s ability to recognize and execute a steering or pedal action in response to a dangerous situation when the autonomous system abruptly requests human intervention. This research used a driving simulator to introduce the concept of level 2 autonomy to a cohort of 60 drivers (male: 48%, female: 52%) of different age groups (teens 16 to 19: 32%, adults: 35 to 54: 37%, seniors 65+: 32%). Participants were surveyed for their perspectives on self-driving vehicles. They were then assessed on a driving simulator that mimicked SAE level 2 of autonomy. Participants’ interaction with the HMI was studied.

Racing continues to be the singular, preeminent source of powertrain development for automakers worldwide. Engineering teams rely on motorsports for the latest prototype testing and research. Endurance racing provides the harshest and most illuminating stage for system design validation of any motorsport competition. While advancements throughout the 20th Century brought about dramatic increases in engine power output, the latest developments from endurance racing may be more impactful for fuel efficiency improvements. Hybrid powertrains are a critical area of research for automakers and are being tested on the toughest of scales. Prototype Powertrain in Motorsport Endurance Racing brings together ten vital SAE technical papers and SAE Automotive Engineering magazine articles surrounding the advancements of hybrid powertrains in motorsports.

Autonomous and/or automated vehicles offer a host of future opportunities but leave many questions unanswered regarding their impact on crash avoidance or the ability of drivers to effectively scan and re-engage from self-driving mode when necessary to avoid crash scenarios. Considering a 16-year-old is several times more likely to die in an automobile crash than other licensed drivers, it was crucial to test both teenage drivers and adults to determine head-on collision avoidance abilities when subjected to a failing autopilot in a simulated autonomous vehicle. In this study, eight teenagers ages 16-19 and four experienced adults underwent four simulated drives (one manual practice drive and three simulated autonomous drives) using a hi-fidelity, Real Time Technologies SimDriver Simulator to represent being in a self-driving vehicle.

Autonomous vehicles (AVs) have already driven millions of miles on public roads, but even the simplest scenarios have not been certified for safety. Current methodologies for the verification of AV’s decision and control systems attempt to divorce the lower level, short-term trajectory planning and trajectory tracking functions from the behavioral rules-based framework that governs mid-term actions. Such analysis is typically predicated on the discretization of the state space and has several limitations. First, it requires that a conservative buffer be added around obstacles such that many feasible plans are classified as unsafe. Second, the discretized controllers modeled in this analysis require several refinement steps before being implementable on an actual AV, and typically do not allow the specification of comfort-related properties on the trajectories. Consumer-ready AVs use motion planning algorithms that generate smooth trajectories.

Inadequate situation awareness and response are increasingly recognized as prevalent critical errors that lead to young driver crashes. To identify and assess key indicators of young driver performance (including situation awareness), we previously developed and validated a Simulated Driving Assessment (SDA) in which drivers are safely and reproducibly exposed to a set of common and potentially serious crash scenarios. Many of the standardized safety measures can be calculated in near real-time from simulator variables. Assessment of situation awareness, however, largely relies on time-consuming data reduction and video coding. Therefore, the objective of this research was to develop a near real-time automated method for analyzing general direction and location of driver's gaze in order to assess situation awareness.

A Hybrid Projectile (HP) is a ballistically launched round that transforms into an Unmanned Aerial Vehicle (UAV) at a designated point during flight. Aerodynamic control surfaces and associated control laws were sought that would extend the projectile's range using body lift and include guidance for a selected point of impact. Several challenges were encountered during the modification of an existing projectile, in this case a 40mm round, to achieve range extension and controllability. The control surfaces must be designed to allow for de-spin, controllability, and natural static stability. Also, a control system with laws and guidance relationships between heading, pitch or glide rate, and the associated aerodynamic surface movements needed to be developed. The designed aerodynamic surfaces, external ballistics, and control methods developed were modeled in a projectile flight simulator built in MATLAB.

Driving simulators offer a safe alternative to on-road driving for the evaluation of performance. In addition, simulated drives allow for controlled manipulations of traffic situations producing a more consistent and objective assessment experience and outcome measure of crash risk. Yet, few simulator protocols have been validated for their ability to assess driving performance under conditions that result in actual collisions. This paper presents results from a new Simulated Driving Assessment (SDA), a 35- to-40-minute simulated assessment delivered on a Real-Time® simulator. The SDA was developed to represent typical scenarios in which teens crash, based on analyses from the National Motor Vehicle Crash Causation Survey (NMVCCS). A new metric, failure to brake, was calculated for the 7 potential rear-end scenarios included in the SDA and examined according two constructs: experience and skill.

Driving simulators provide a safe, highly reproducible environment in which to assess driver behavior. Nevertheless, data reduction to standardized metrics can be time-consuming and cumbersome. Further, the validity of the results is challenged by inconsistent definitions of metrics, precluding comparison across studies and integration of data. No established tool has yet been made available and kept current for the systematic reduction of literature-derived safety metrics. The long term goal of this work is to develop DriveLab, a set of widely applicable routines for reducing simulator data to expert-approved metrics. Since Matlab™ is so widely used in the research community, it was chosen as a suitable environment. This paper aims to serve as a case study of data reduction techniques and programming choices that were made for simulator analysis of a specific research project, the Simulated Driving Assessment.

Heavy duty tractor-trailers under freeway operations consume about 65% of the total engine shaft energy to overcome aerodynamic drag force. Vehicles are exposed to on-road crosswinds which cause change in pressure distribution with a relative wind speed and yaw angle. The objective of this study was to analyze the drag losses as a function of on-road wind conditions, on-road vehicle position and trajectory. Using coefficient of drag (CD) data available from a study conducted at NASA Ames, Geographical Information Systems model, time-varying weather data and road data, a generic model was built to identify the yaw angles and the relative magnitude of wind speed on a given route over a given time period. A region-based analysis was conducted for a study on interstate trucking operation by employing I-79 running through West Virginia as a case study by initiating a run starting at 12am, 03/03/2012 out to 12am, 03/05/2012.

Both traumatic and slow-onset disc herniation can directly compress and/or chemically irritate cervical nerve roots, and both types of root injury elicit pain in animal models of radiculopathy. This study investigated the relative contributions of mechanical compression and chemical irritation of the nerve root to spinal regulation of neuronal activity using several outcomes. Modifications of two proteins known to regulate neurotransmission in the spinal cord, the neuropeptide calcitonin gene-related peptide (CGRP) and glutamate transporter 1 (GLT-1), were assessed in a rat model after painful cervical nerve root injuries using a mechanical compression, chemical irritation or their combination of injury. Only injuries with compression induced sustained behavioral hypersensitivity (p≤0.05) for two weeks and significant decreases (p<0.037) in CGRP and GLT-1 immunoreactivity to nearly half that of sham levels in the superficial dorsal horn.

This paper presents 3D finite element analysis performed for a composite cylindrical tank made of 6061-aluminum liner overwrapped with carbon fibers subjected to a burst internal pressure of 1610 bars. As the service pressure expected in these tanks is 700 bars, a factor of safety of 2.3 is kept the same for all designs. The optimal design configuration of such high pressure storage tanks includes an inner liner used as a gas permeation barrier, geometrically optimized domes, inlet/outlet valves with minimum stress concentrations, and directionally tailored exterior reinforcement for high strength and stiffness. Filament winding of pressure vessels made of fiber composite materials is the most efficient manufacturing method for such high pressure hydrogen storage tanks. The complexity of the filament winding process in the dome region is characterized by continually changing the fiber orientation angle and the local thickness of the wall.

The study was aimed at assessing in-use emissions of a USEPA 2010 emissions-compliant heavy-duty diesel vehicle powered by a model year (MY) 2011 engine using West Virginia University's Transportable Emissions Measurement System (TEMS). The TEMS houses full-scale CVS dilution tunnel and laboratory-grade emissions measurement systems, which are compliant with the Code of Federal Regulation (CFR), Title 40, Part 1065 [1] emissions measurement specifications. One of the specific objectives of the study, and the key topic of this paper, is the quantification of greenhouse gas (GHG) emissions (CO2, N2O and CH4) along with ammonia (NH3) and regulated emissions during real-world operation of a long-haul heavy-duty vehicle, equipped with a diesel particulate filter (DPF) and urea based selective catalytic reduction (SCR) aftertreatment system for PM and NOx reduction, respectively.

Industries related to automotive manufacturing and its supply chain play a key role in leaving a carbon footprint during an automobile's life cycle. Per the report from Lawrence Berkeley National Laboratory (LBNL) in March, 2008 [1], “motor vehicle industry in the U.S. spends about $3.6 billion on energy annually.” The proposed research will focus on energy savings opportunities in automotive manufacturing and its supplier network. The US Department of Energy (DOE) funds 24 Industrial Assessment Centers (IAC) throughout the U.S. that conduct energy assessments at many of these facilities. The results of these assessments are summarized in a database maintained by Rutgers University which acts as the central management body for all the IACs. This research will present key concepts summarized from this database.

The hydrogen economy envisioned in the future requires safe and efficient means of storing hydrogen fuel for either use on-board vehicles, delivery on mobile transportation systems or high-volume storage in stationary systems. The main emphasis of this work is placed on the high -pressure storing of gaseous hydrogen on-board vehicles. As a result of its very low density, hydrogen gas has to be stored under very high pressure, ranging from 350 to 700 bars for current systems, in order to achieve practical levels of energy density in terms of the amount of energy that can be stored in a tank of a given volume. This paper presents 3D finite element analysis performed for a composite cylindrical tank made of 6061-aluminum liner overwrapped with carbon fibers subjected to a burst internal pressure of 1610 bars. As the service pressure expected in these tanks is 700 bars, a factor of safety of 2.3 is kept the same for all designs.

The new General Motors 2-mode hybrid transmission for front-wheel-drive vehicles has been incorporated into a 2009 Saturn Vue by the West Virginia University EcoCAR team. The 2-mode hybrid transmission can operate in either one of two electrically variable transmission modes or four fixed gear modes although only the electrically variable modes were explored in this paper. Other major power train components include a GM 1.3L SDE turbo diesel engine fueled with B20 biodiesel and an A123 Systems 12.9 kWh lithium-ion battery system. Two additional vehicle controllers were integrated for tailpipe emission control, CAN message integration, and power train hybridization control. Control laws for producing maximum fuel efficiency were implemented and include such features as engine auto-stop, regenerative braking and optimized engine operation. The engine operating range is confined to a high efficiency area that improves the overall combined engine and electric motor efficiency.

Automotive industries in the US and around the world have enormous impact on the economy of each country. Not just the major vehicle manufacturer, but all the other companies in the supply chain are equally important. This was evident with the earthquake and Tsunami that happened in March 2011. Because of the massive destruction at suppliers' facilities, the automakers in the US and other countries struggled to get the necessary parts and supplies. This created a ripple effect throughout the world and led to the closure of several automakers' facilities for a long time. Thus, the automotive supply chains are as important as the main automotive manufacturing facilities. Since these suppliers produce a lot of parts and supplies, the corresponding energy usage is also significant. The current research is focused on compressed air and process heating system analysis at one of the automotive parts manufacturer in Mexico.

Diesel Particulate Filters (DPFs) are well assessed exhaust aftertreatment devices currently equipping almost every modern diesel engine to comply with the most stringent emission standards. However, an accurate estimation of soot content (loading) is critical to managing the regeneration of DPFs in order to attain optimal behavior of the whole engine-after-treatment assembly, and minimize fuel consumption. Real-time models can be used to address challenges posed by advanced control systems, such as the integration of the DPF with the engine or other critical aftertreatment components or to develop model-based OBD sensors. One of the major hurdles in such applications is the accurate estimation of engine Particulate Matter (PM) emissions as a function of time. Such data would be required as input data for any kind of accurate models. The most accurate way consists of employing soot sensors to gather the real transient soot emissions signal, which will serve as an input to the model.

This research attempts to investigate the effect of change in system curve on the energy intensity method of measurement and verification of energy savings. With recent push from US government on energy efficiency through EPACT 2007 and upturn in performance contracted energy efficiency project implementations the effective and accurate evaluation of energy savings as compared to the baseline is of paramount importance. The authors have studied different methods of Measurement and Verification (M&V) of energy savings from literature to compare and contrast and clearly bring out merits and de-merits of each. Finally, the role of production level variable plays in establishing the baseline energy usage is discussed. Though modern models proposed in the literature of determining baseline energy usage consider production level, this variable is compounded from two variables viz., time of usage of a system and fraction of total capacity usage.