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In this paper a yaw stability control algorithm along with an emergency roll control strategy have been developed. The yaw stability controller and emergency roll controller were both developed using linear two degree-of-freedom vehicle models. The yaw stability controller is based on Lyapunov stability criteria and uses vehicle lateral acceleration and yaw rate measurements to calculate the corrective yaw moment required to stabilize the vehicle yaw motion. The corrective yaw moment is then applied by means of a differential braking strategy in which one wheel is selected to be braked with appropriate brake torque applied. The emergency roll control strategy is based on a rollover coefficient related to vehicle static stability factor. The emergency roll control strategy utilizes vehicle lateral acceleration measurements to calculate the roll coefficient. If the roll coefficient exceeds some predetermined threshold value the emergency roll control strategy will deploy.

Autonomous vehicles have the potential to transform lives by providing transportation to a wider range of users. However, with this new method of transportation, user acceptance and comfort are critical for widespread adoption. This exploratory study aims to investigate what makes passengers uncomfortable in existing vehicles to inform the design of future autonomous vehicles. In order to predict what may impact user acceptance for a diverse rider population for future autonomous vehicles, it is important to understand what makes a broad range of passengers uncomfortable today. In this study, interviews were conducted for a total of 75 participants from three diverse groups, including 20 automotive engineering graduate students who are building an autonomous concept vehicle, 21 non-technical adults, and 34 senior citizens. The results revealed both topics which made different groups of passengers uncomfortable as well as how these varied between the groups.

The purpose of this research is to characterize the wear resistance of wheel treads made of polymeric woven and non-woven fabrics. Experimental research is used to characterize two wear mechanisms: (1) external wear due to large sliding between the tread and rocks, and (2) external wear due to small sliding between the tread and abrasive sand. Experimental setups include an abrasion tester and a small-scale merry-go-round where the tread is attached to a deformable rolling wheel. The wear resistance is characterized using various measures including, quantitatively, by the number of cycles to failure, and qualitatively, by micro-visual inspection of the fibers’ surface. This paper describes the issues related to each experiment and discusses the results obtained with different polymeric materials, fabric densities and sizes. The predominant wear mechanism is identified and should then be used as one of the criteria for further design of the tread.

In the next 20 years fully autonomous vehicles are expected to be in the market. The advance on their development is creating paradigm shifts on different automotive related research areas. Vehicle interiors design and human vehicle interaction are evolving to enable interaction flexibility inside the cars. However, most of today’s vehicle manufacturers’ autonomous car concepts maintain the steering wheel as a control element. While this approach allows the driver to take over the vehicle route if needed, it causes a constraint in the previously mentioned interaction flexibility. Other approaches, such as the one proposed by Google, enable interaction flexibility by removing the steering wheel and accelerator and brake pedals. However, this prevents the users to take control over the vehicle route if needed, not allowing them to make on-route spontaneous decisions, such as stopping at a specific point of interest.

Artificial intelligence (AI) enhanced control system deployments are emerging as a viable substitute to more traditional control system. In particular, deep learning techniques offer an alternate approach to tune the ever increasing sets of control system parameters to extract performance. However, the systematic verification and validation (to establish the reliability and robustness) of deep learning based controllers in actual deployments remains a challenge. This is exacerbated by the need to evaluate and optimize control systems embedded within an operational environment (with its own sets of additional unknown or uncertain parameters). Existing literature comparisons of deep learning against traditional controllers, where they may exist, do not offer structured approaches to comparative performance evaluation and improvement. It is also crucial to develop a standardized controlled test environment within which various controllers are evaluated against a common metric.

Improving passenger safety inside vehicle cabins requires continuously monitoring vehicle seat occupancy statuses. Monitoring a vehicle seat’s occupancy status includes detecting if the seat is occupied and classifying the seat’s occupancy type. This paper introduces an innovative non-intrusive technique that employs capacitive sensing and an occupancy classifier to monitor a vehicle seat’s occupancy status. Capacitive sensing is facilitated by a meticulously constructed capacitance-sensing mat that easily integrates with any vehicle seat. When a passenger or an inanimate object occupies a vehicle seat equipped with the mat, they will induce variations in the mat’s internal capacitances. The variations are, in turn, represented pictorially as grayscale capacitance-sensing images (CSI), which yield the feature vectors the classifier requires to classify the seat’s occupancy type.

Vehicle safety remains a significant concern for consumers, government agencies, and automotive manufacturers. One critical type of vehicle accident results from the right or left side tires leaving the road surface and then returning abruptly due to large steering wheel inputs (road runoff and return). A subset of runoff road crashes that involve a steep hard shoulder has been labeled shoulder induced accidents. In this paper, a limited authority real time steering controller has been developed to mitigate shoulder induced accidents. A Kalman Filter based tire cornering stiffness estimation technique has been coupled with a feedback controller and driver intention module to create a safer driving solution without excessive intervention. In numerical studies, lateral vehicle motion improvements of 30% were realized for steering intervention. Specifically, the vehicle crossed the centerline after 1.0 second in the baseline case versus 1.3 seconds with steering assistance at 60 kph.

Computational Fluid Dynamics (CFD) modeling was used to investigate the effects of the direct injection of wet ethanol at various injection timings during the intake stroke in a diesel engine with a shallow bowl piston. Thermally Stratified Compression Ignition (TSCI) has been proposed to expand the operating range of Low Temperature Combustion (LTC) by broadening the temperature distribution in the cylinder prior to ignition. TSCI is accomplished by injecting either water or a water-fuel mixture with a high latent heat of vaporization like wet ethanol. This current study focuses on isolating the effects that injecting such a high heat of vaporization mixture during the intake stroke has on the distribution of temperature and equivalence ratio in the cylinder before the onset of combustion. A CONVERGE 3-D CFD model of a single cylinder diesel research engine using Reynolds Averaged Naiver Stokes (RANS) turbulence modeling was developed and validated against experimental data.

The United States Research, Development, and Engineering Command's Tank Automotive Research, Development and Engineering Center (U.S. Army RDECOM-TARDEC) laboratories, in accordance with a Science and Technology Objective (STO), are looking for both real-time and non real-time modeling and simulation methods to advance the capabilities and methodologies used in the Army's Modeling and Simulation areas. Advancing technologies require TARDEC to model new components and vehicles that may be significantly different from prior systems. TARDEC's ultimate goal is to develop the capability to model and accurately recreate the behaviors of advance technologies that may present themselves in the Army's Transformation and its Future Combat System (FCS) of vehicles in real-time with the soldier-in-the-loop. This paper discusses TARDEC's effort to accomplish this goal.

Image segmentation has historically been a technique for analyzing terrain for military autonomous vehicles. One of the weaknesses of image segmentation from camera data is that it lacks depth information, and it can be affected by environment lighting. Light detection and ranging (LiDAR) is an emerging technology in image segmentation that is able to estimate distances to the objects it detects. One advantage of LiDAR is the ability to gather accurate distances regardless of day, night, shadows, or glare. This study examines LiDAR and camera image segmentation fusion to improve an advanced driver-assistance systems (ADAS) algorithm for off-road autonomous military vehicles. The volume of points generated by LiDAR provides the vehicle with distance and spatial data surrounding the vehicle.

This document presents the results of computer-based, vehicle dynamics performance assessments of Future Truck concepts with such features as a variable height, hydraulic, trailing arm suspension, skid steering, and in-hub electric drive motors. Fully three-dimensional Future Truck models were created using a commercially available modeling and simulation methodology and limited validation studies were performed by comparing model predictions with baseline, validated model predictions from another vehicle in the same size and class as the Future Truck concept vehicles. The models were considered accurate enough to predict various aspects of ride quality and stability performance, critical to US Army Objective Force mission needs. One-to-one comparisons of the Future Truck concepts and a standard, solid-axle, Heavy Tactical Vehicle (HTV) operating in various terrain and obstacle negotiation conditions were performed.

The application of virtual engineering methods can streamline the product design process through improved collaboration opportunities among the technical staff and facilitate additive manufacturing processes. A product digital twin can be created using the available computer-aided design and analytical mathematical models to numerically explore the current and future system performance based on operating cycles. The strategic decision to implement a digital twin is of interest to companies, whether the required financial and workforce resources will be worthwhile. In this paper, two metrics are introduced to assist management teams in evaluating the technology potential. The usefulness and time savings metrics will be presented with accompanying definitions. A case study highlights the usefulness metric for the “Deep Orange” prototype vehicle, an innovative off-road hybrid vehicle designed and fabricated at Clemson University.

Non-linear model predictive engine control (nMPC) systems have the ability to reduce calibration effort while improving transient engine response. The main drawback of nMPC for engine control is the computational power required to realize real-time operation. Most of this computational power is spent linearizing the non-linear plant model at each time step. Additionally, the effectiveness of the nMPC system relies heavily on the accuracy of the model(s) used to predict the future system behavior, which can be difficult to model physically. This paper introduces a hybrid modeling approach for internal combustion engines that combines physics-based and machine learning techniques to generate accurate models that can be linearized with low computational power. This approach preserves the generalization and robustness of physics-based models, while maintaining high accuracy of data-driven models. Advantages of applying the proposed model with nMPC are discussed.

Automotive drive-by-wire systems have enabled greater mobility options for individuals with physical disabilities. To further expand the driving paradigm, a need exists to consider an alternative vehicle steering mechanism to meet specific needs and constraints. In this study, a cellphone steering controller was investigated using a fixed-base driving simulator. The cellphone incorporated the direction control of the vehicle through roll motion, as well as the brake and throttle functionality through pitch motion, a design that can assist disabled drivers by excluding extensive arm and leg movements. Human test subjects evaluated the cellphone with conventional vehicle control strategy through a series of roadway maneuvers. Specifically, two distinctive driving situations were studied: a) obstacle avoidance test, and b) city road traveling test. A conventional steering wheel with self-centering force feedback tuning was used for all the driving events for comparison.

This study was driven by the prevalence of older drivers’ overrepresentation in crashes caused by pedal application errors. Previous research has shown tasks prone to pedal errors, which include emergency braking, parking lot maneuvers and reaching out of the driver’s window. However, pedal usage characteristics of older drivers while performing on-road driving tasks are unknown. The objective of this research was to understand pedal usage characteristics of older drivers during on-road driving tasks in an instrumented vehicle. Twenty-six drivers over the age of 60 completed 10 stopping tasks as the baseline for stopping performance, a startle-braking task, two forward parking tasks and two reaching out of the vehicle tasks. Results for this instrumented vehicle study showed significantly positive correlations between stature and the percent of foot pivoting, and between shoe length and percent of foot pivoting in the baseline stopping tasks.

Unmanned ground vehicles (UGVs) may encounter difficulties accommodating environmental uncertainties and system degradations during harsh conditions. However, human experience and onboard intelligence can may help mitigate such cases. Unfortunately, human operators have cognition limits when directly supervising multiple UGVs. Ideally, an automated decision aid can be designed that empowers the human operator to supervise the UGVs. In this paper, we consider a connected UGV platoon under cyber attacks that may disrupt safety and degrade performance. An observer-based resilient control strategy is designed to mitigate the effects of vehicle-to-vehicle (V2V) cyber attacks. In addition, each UGV generates both internal and external evaluations based on the platoons performance metrics. A cloud-based trust-based information management system collects these evaluations to detect abnormal UGV platoon behaviors.

In the United States and worldwide, 38,824 and 1.35 million people were killed in vehicle crashes during 2020. These statistics are tragic and indicative of an on-going public health crisis centered on automobiles and other ground transportation solutions. Although the long-term US vehicle fatality rate is slowly declining, it continues to be elevated compared to European countries. The introduction of vehicle safety systems and re-designed roadways has improved survivability and driving environment, but driver behavior has not been fully addressed. A non-confrontational approach is the evaluation of driver behavior using onboard sensors and computer algorithms to determine the vehicle’s “mistrust” level of the given operator and the safety of the individual operating the vehicle. This is an inversion of the classic human-machine trust paradigm in which the human evaluates whether the machine can safely operate in an automated fashion.

In military applications, diesel engines are required to achieve high power outputs and therefore must operate at high loads. This high load operation leads to high piston component temperatures and heat rejection rates limiting the packaged power density of the powertrain. To help predict and understand these constraints, as well as their effects on performance, a thermodynamic engine model coupled to a finite element heat conduction solver is proposed and validated in this work. The finite element solver is used to calculate crank angle resolved, spatially averaged piston temperatures from in-cylinder heat transfer calculations. The calculated piston temperatures refine the heat transfer predictions as well requiring iteration between the thermodynamic model and finite element solver.

In this work, a novel opposed piston architecture is proposed where one crankshaft rotates at twice the speed of the other. This results in one piston creating a 2-stroke profile and another with a 4-stroke profile. In this configuration, the slower piston operates in the 2-stroke CAD domain, while the faster piston completes 2 reciprocating cycles in the same amount of time (4-stroke). The key benefit of this cycle is that the 4-stroke piston increases the rate of compression and expansion (dV/dθ), which lowers the combustion-induced pressure rise rate after top dead center (crank angle location of minimum volume). Additionally, it lowers in-cylinder temperatures and pressures more rapidly, resulting in a lower residence time at high temperatures, which reduces residence time for thermal NOx formation and reduces the temperature differential between the gas and the wall, thereby reducing heat transfer.

The operation of internal combustion engines depend on the successful management of the fuel, spark, and cooling processes to ensure acceptable performance, emission levels, and fuel economy. Two different thermal management systems exist for engines - air and liquid cooling. Smaller displacement utility and spark ignition aircraft engines typically feature air cooled systems which rely on forced convection over the exterior engine surfaces. In contrast, passenger/light-duty engines use a water-ethylene glycol mixture which circulates through the radiator, water pump, and heater core. The regulation of the overall engine temperature, based on the coolant's temperature, has been achieved with the thermostat valve and (electric) radiator fan. To provide insight into the thermal behavior of the cylinder-head assembly for enhanced cooling system operation, a dynamic model must exist.