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The Lane Change Task (LCT) provides a simple, scorable simulation of driving, and serves as a primary task in studies of driver distraction. It is widely accepted, but somewhat limited in functionality, a problem this project partially overcomes. In the Lane Change Task, subjects drive along a road with 3 lanes in the same direction. Periodically, signs appear, indicating in which of the 3 lanes the subject should drive, which changes from sign to sign. The software is plug-and-play for a current Windows computer with a Logitech steering/pedal assembly, even though the software was written 18 years ago. For each timestamp in a trial, the software records the steering wheel angle, speed, and x and y coordinates of the subject. A limitation of the LCT is that few characteristics of this useful software can be readily modified as only the executable code is available (on the ISO 26022 website), not the source code.

Making manned and remotely-controlled wheeled and tracked vehicles easier to drive, especially off-road, is of great interest to the U.S. Army. If vehicles are easier to drive (especially closed hatch) or if they are driven autonomously, then drivers could perform additional tasks (e.g., operating weapons or communication systems), leading to reduced crew sizes. Further, poorly driven vehicles are more likely to get stuck, roll over, or encounter mines or improvised explosive devices, whereby the vehicle can no longer perform its mission and crew member safety is jeopardized. HMI technology and systems to support human drivers (e.g., autonomous driving systems, in-vehicle monitors or head-mounted displays, various control devices (including game controllers), navigation and route-planning systems) need to be evaluated, which traditionally occurs in mission-specific (and incomparable) evaluations.

Automated vehicles (AVs)—and the automated driving systems (ADSs) that enable them—are increasing in prevalence but remain far from ubiquitous. Progress has occurred in spurts, followed by lulls, while the motor transportation system learns to design, deploy, and regulate AVs. Automated Vehicles: A Human/Machine Co-learning Experience focuses on how engineers, regulators, and road users are all learning about a technology that has the potential to transform society. Those engaged in the design of ADSs and AVs may find it useful to consider that the spurts and lulls and stakeholder tussles are a normal part of technology transformations; however, this report will provide suggestions for effective stakeholder engagement. Click here to access the full SAE EDGETM Research Report portfolio.

Tanker trucks are commonly used for transporting liquid material including chemical and petroleum products. On the one hand, tanker trucks are susceptible to rollover accidents due to the high center of gravity when they are loaded and due to the liquid sloshing effects when the tank is partially filled. On the other hand, tanker truck rollover accidents are among the most dangerous vehicle crashes, frequently resulting in serious to fatal driver injuries and significant property damage, because the liquid cargo is often hazardous and flammable. Therefore, effective schemes for tanker truck rollover avoidance are highly desirable and can bring a considerable amount of societal benefit. Yet, the development of such schemes is challenging, as tanker trucks can operate in various environments and be affected by manufacturing variability, aging, degradation, etc. This paper considers the use of Learning Reference Governor (LRG) for tanker truck rollover avoidance.

Vehicle surface contamination can degrade not only soldier vision but also the effectiveness of camera and sensor systems mounted externally on the vehicle for autonomy and situational awareness. In order to control vehicle surface contamination, a better understanding of dust particle generation, transport and accumulation is necessary. The focus of the present work is simulation of vehicle surface contamination on the rear part of the vehicle due to the interaction of the combat vehicle track with the ground and dust in the surrounding ambient atmosphere. A notional tracked military vehicle is used for the Computational fluid dynamics (CFD) simulation. A CFD methodology with one-way-coupled Lagrangian particle modeling is used. The simulation is initially run with only air flow to solve the air pressure, velocity, and turbulence quantities in a steady state condition.

This study uses full drive cycle simulation to compare the fuel consumption of a vehicle with a turbocharged (TC) engine to the same vehicle with an alternative boosting technology, namely, a hybrid supercharger, in which a planetary gear mechanism governs the power split to the supercharger between the crankshaft and a 48 V 5 kW electric motor. Conventional mechanically driven superchargers or electric superchargers have been proposed to improve the dynamic response of boosted engines, but their projected fuel efficiency benefit depends heavily on the engine transient response and driver/cycle aggressiveness. The fuel consumption benefits depend on the closed-loop engine responsiveness, the control tuning, and the torque reserve needed for each technology. To perform drive cycle analyses, a control strategy is designed that minimizes the boost reserve and employs high rates of combustion dilution via exhaust gas recirculation (EGR).

This paper presents a simple method of using Voronoi partitions for estimating vehicle fuel economy from a limited set of engine operating conditions. While one of the overarching goals of engine research is to continually improve vehicle fuel economy, evaluating the impact of a change in engine operating efficiency on the resulting fuel economy is a non-trivial task and typically requires drive cycle simulations with experimental data or engine model predictions and a full suite of engine controllers over a wide range of engine speeds and loads. To avoid the cost of collecting such extensive data, proprietary methods exist to estimate fuel economy from a limited set of engine operating conditions. This study demonstrates the use of Voronoi partitions to cluster and quantize the fuel consumed along a complex trajectory in speed and load to generate fuel consumption estimates based on limited simulation or experimental results.

A full-system, end-to-end blast modeling and simulation of vehicle underbody buried blast events typically includes detailed modeling of soil, high explosive (HE) charge and air. The complex computations involved in these simulations take days to just capture the initial 50-millisecond blast-off phase, and in some cases, even weeks. The single most intricate step in the buried blast event simulation is in the modeling of the explosive loading on the underbody structure from the blast products; it is also one of the most computationally expensive steps of the simulation. Therefore, there is significant interest in the modeling and simulation community to develop various methodologies for fast running tools to run full simulation events in quicker turnarounds of time.

As part of its midterm evaluation of the 2022-2025 light-duty greenhouse gas (GHG) standards, the Environmental Protection Agency (EPA) has been acquiring fuel efficiency data from testing of recent engines and vehicles. The benchmarking data are used as inputs to EPA’s Advanced Light Duty Powertrain and Hybrid Analysis (ALPHA) vehicle simulation model created to estimate GHG emissions from light-duty vehicles. For complete powertrain modeling, ALPHA needs both detailed engine fuel consumption maps and transmission efficiency maps. EPA’s National Vehicle and Fuels Emissions Laboratory has previously relied on contractors to provide full characterization of transmission efficiency maps. To add to its benchmarking resources, EPA developed a streamlined more cost-effective in-house method of transmission testing, capable of gathering a dataset sufficient to broadly characterize transmissions within ALPHA.

Mercury is a high-fidelity, physics-based object-oriented software for conducting simulations of vehicle performance evaluations for requirements and engineering metrics. Integrating cutting-edge, massively parallel modeling techniques for soft, cohesive and dry granular soil that will integrate state-of-the-art soil simulation with high-fidelity multi-body dynamics and powertrain modeling to provide a comprehensive mobility simulator for ground vehicles. The Mercury implements the Chrono::Vehicle dynamics library for vehicle dynamics, which provides multi-body dynamic simulation of wheeled and tracked vehicles. The powertrain is modeled using the Powertrain Analysis Computational Environment (PACE), a behavior-based powertrain analysis based on the U.S. Department of Energy’s Autonomie software. Vehicle -terrain interaction (VTI) is simulated with the Ground Contact Element (GCE), which provides forces to the Chrono-vehicle solver.

The U.S. Environmental Protection Agency’s (EPA’s) Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) tool was created to estimate greenhouse gas (GHG) emissions from light-duty vehicles. ALPHA is a physics-based, forward-looking, full vehicle computer simulation capable of analyzing various vehicle types with different powertrain technologies, showing realistic vehicle behavior, and auditing of all energy flows in the model. In preparation for the midterm evaluation (MTE) of the 2017-2025 light-duty GHG emissions rule, ALPHA has been refined and revalidated using newly acquired data from model year 2013-2016 engines and vehicles. The robustness of EPA’s vehicle and engine testing for the MTE coupled with further validation of the ALPHA model has highlighted some areas where additional data can be used to add fidelity to the engine model within ALPHA.

For existing fleets such as the U.S. military ground vehicle fleet, there are few ways to reduce vehicle fuel consumption that don’t involve expensive retrofitting. Replacing standard lubricants with those that can achieve higher vehicle efficiencies is one practical and inexpensive way to improve fleet fuel efficiency. In an effort to identify axle gear lubricants that can reduce the fuel consumption of its fleet, the U.S. Army is developing a stationary axle efficiency test stand and procedure. In order to develop this capability, on-track vehicle fuel consumption testing was completed using light, medium, and heavy tactical wheeled vehicles following a modified SAE J1321 type test procedure. Tested lubricants included a baseline SAE 80W-90, a fuel efficient SAE 75W-90, and a fuel efficient SAE 75W-140. Vehicle testing resulted in reductions in fuel consumption of up to 2%.

Mcity at the University of Michigan in Ann Arbor provides a realistic off-roadway environment in which to test vehicles and drivers in complex traffic situations. It is intended for testing of various levels of vehicle automation, from advanced driver assistance systems (ADAS) to fully self-driving vehicles. In a recent human factors study of interfaces for teen drivers, we performed parallel experiments in a driving simulator and Mcity. We implemented driving scenarios of moderate complexity (e.g., passing a vehicle parked on the right side of the road just before a pedestrian crosswalk, with the parked vehicle partially blocking the view of the crosswalk) in both the simulator and at Mcity.

Motion cueing algorithms can improve the perceived realism of a driving simulator, however, data on the effects on driver performance and simulator sickness remain scarce. Two novel motion cueing algorithms varying in concept and complexity were developed for a limited maneuvering workspace, hexapod/Stuart type motion platform. The RideCue algorithm uses a simple swing motion concept while OverTilt Track algorithm uses optimal pre-positioning to account for maneuver characteristics for coordinating tilt adjustments. An experiment was conducted on the US Army Tank Automotive Research, Development and Engineering Center (TARDEC) Ride Motion Simulator (RMS) platform comparing the two novel motion cueing algorithms to a pre-existing algorithm and a no-motion condition.

A GT-SUITE vehicle-aftertreatment model has been developed to examine the cold-start emissions reduction capabilities of a Vacuum Insulated Catalytic Converter (VICC). This converter features a thermal management system to maintain the catalyst monolith above its light-off temperature between trips so that most of a vehicle’s cold-start exhaust emissions are avoided. The VICC thermal management system uses vacuum insulation around the monoliths. To further boost its heat retention capacity, a metal phase-change material (PCM) is packaged between the monoliths and vacuum insulation. To prevent overheating of the converter during periods of long, heavy engine use, a few grams of metal hydride charged with hydrogen are attached to the hot side of the vacuum insulation. The GT-SUITE model successfully incorporated the transient heat transfer effects of the PCM using the effective heat capacity method.

This set consists of three books, Design of Automotive Composites, CAE Design and Failure Analysis of Automotive Composites, and Biocomposites in Automotive Applications all developed by Dr. Charles Lu and Dr. Srikanth Pilla. Design of Automotive Composites reports successful designs of automotive composites occurred recently in this arena, CAE Design and Failure Analysis of Automotive Composites focuses on the latest use of CAE (Computer-Aided Engineering) methods in design and failure analysis of composite materials and structures, and Biocomposites in Automotive Applications, focuses on processing and characterization of biocomposites, their application in the automotive industry and new perspectives on automotive sustainability. Together, they are a focused collection providing the reader with must-read technical papers, hand-picked by the editors, supporting the growing importance of the use of composites in the ground vehicle industry. Dr. Charles Lu is H.E.

Combatting the modification of automotive control systems is a current and future challenge for OEMs and suppliers. ‘Chip-tuning’ is a manifestation of manipulation of a vehicle's original setup and calibration. With the increase in automotive functions implemented in software and corresponding business models, chip tuning will become a major concern. Recognizing and reporting of tuned control units in a vehicle is required for technical as well as legal reasons. This work approaches the problem by capturing the behavior of relevant control units within a machine learning system called a recognition module. The recognition module continuously monitors vehicle's sensor data. It comprises a set of classifiers that have been trained on the intended behavior of a control unit before the vehicle is delivered. When the vehicle is on the road, the recognition module uses the classifier together with current data to ascertain that the behavior of the vehicle is as intended.

According to the National Highway Traffic Safety Administration (NHTSA), motor collisions account for nearly 2.4 million injuries and 37 thousand fatalities each year in the United States. A great deal of research has been done in the area of vehicular safety, but very little has been completed to ensure licensed drivers are properly trained. Given the inherent risks in driving itself, the test for licensure should be uniform and consistent. To address this issue, an inexpensive, portable data acquisition and analysis system has been developed for the evaluation of driver performance. A study was performed to evaluate the system, and each participant was given a normalized driver rating. The average driver rating was μ=55.6, with a standard deviation of σ=12.3. All but 3 drivers fell into the so-called “Target Zone”, defined by a Driver Rating of μ± 1σ.

Driver modeling is essential to both vehicle design and control unit development. It can improve the understanding of human driving behavior and decrease the cost and risk of vehicle system verification and validation. In this paper, three driver models were implemented to simulate the behavior of drivers subject to a run-off-road recovery event. Target path planning, pursuit behavior, compensate behavior, physical limitations, and neuromuscular modeling were taken into consideration in the feedforward/feedback driver model. A transfer function driver model and a cost function based driver model from a popular vehicle simulation software were also simulated and a comparison of these three models was made. The feedforward/feedback driver model exhibited the best balance of performance with smallest overshoot (0.226m), medium settling time (1.20s) and recovery time (4.30s).

Driving skills and driving experience develop differently between a civilian and a military service member. Since 2000, the Department of Defense reports that two-thirds of non-related to war fatalities among active duty service members were due to transportation-related incidents. In addition, vehicle crashes are the leading non-related to war cause of both fatalities and serious injuries among active duty Marines. A pilot safe driving program for Marines was jointly developed by the Richard Petty Driving Experience and Clemson University Automotive Safety Research Institute. The pilot program includes four modules based on leading causes of vehicle crashes, and uses classroom and behind the wheel components to improve and reinforce safe driving skills and knowledge. The assessment results of this pilot program conducted with 192 Marines in September 2011 at Camp LeJeune, NC are presented and discussed.