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Accurate in-cylinder air charge estimation is important for engine torque determination, controlling air-to-fuel ratio, and ensuring high after-treatment efficiency. Spark ignition (SI) engine technologies like variable valve timing (VVT) and exhaust gas recirculation (EGR) are applied to improve fuel economy and reduce pollutant emissions, but they increase the complexity of air charge estimation. Increased air-path complexity drives the need for cost effective solutions that produce high air mass prediction accuracy while minimizing sensor cost, computational effort, and calibration time. A large number of air charge estimation techniques have been developed using a range of sensors sets combined with empirical and/or physics-based models. This paper provides a technical review of research in this area, focused on SI engines.

Fuel efficiency improvement in automobiles has been a topic of great interest over the past few years, especially with the introduction of the new CAFE 2025 standards. Although there are multiple ways of improving the fuel efficiency of an automobile, lightweighting is one of the most common approaches taken by many automotive manufacturers. Lightweighting is even more significant in electric vehicles as it directly affects the range of the vehicle. Amidst this context of lightweighting, the use of composite materials as alternatives to metals has been proven in the past to help achieve substantial weight reduction. The focus of using composites for weight reduction has however been typically limited to major structural components, such as BiW and closures, due to high material costs. Secondary structural components which contribute approximately 30% of the vehicle weight are usually neglected by these weight reduction studies.

This paper describes a computational and experimental effort to document the detailed flow field around a pickup truck. The major objective was to benchmark several different computational approaches through a series of validation simulations performed at Clemson University (CU) and overseen by those performing the experiments at the GM R&D Center. Consequently, no experimental results were shared until after the simulations were completed. This flow represented an excellent test case for turbulence modeling capabilities developed at CU. Computationally, three different turbulence models were employed. One steady simulation used the realizable k-ε model. The second approach was an unsteady RANS simulation, which included a turbulence closure model developed in-house. This simulation captured the unsteady shear layer rollup and breakdown over the front of the hood that was expected and seen in the experiments but unattainable with other off-the-shelf turbulence models.

In this paper, we review the literature related to the reuse of computer-based simulation models in the context of systems design. Models are used to capture aspects of existing or envisioned systems and are simulated to predict the behavior of these systems. However, developing such models from scratch requires significant time and effort. Researchers have recognized that the time and effort can be reduced if existing models or model components are reused, leading to the study of model reusability. In this paper, we review the tasks necessary to retrieve and reuse model components from repositories, and to prepare new models and model components such that they are more amenable for future reuse. Model reuse can be significantly enhanced by carefully characterizing the model, and capturing its meaning and intent so that potential users can determine whether the model meets their needs.

A safe driver program has been established through a public-private partnership. This program targets novice drivers and uses a combination of classroom and in-vehicle training exercises to address critical driver errors known to lead to crashes. Students participate in four modules: braking to learn proper stopping technique, obstacle avoidance / reaction time to facilitate proper lane selection and collision avoidance, tailgating to learn about following distances, and loss of control to react appropriately when a vehicle is about to become laterally unstable. Knowledge pre and posttests are also administered at the start and end of the program. Students' in-vehicle driving performance are evaluated by instructors as well as recorded by onboard data acquisition units. The data has been evaluated with objective and subjective grading rubrics. The 70 participants in three classes used as a case study achieved an average skill score of 83.93/100.

This manuscript provides a review of different types and categorization of the chassis dynamometer systems. The review classifies the chassis dynamometers based on the configuration, type of rollers and the application type. Additionally the manuscript discusses several application examples of the chassis dynamometer including: performance and endurance mileage accumulation tests, fuel efficiency and exhaust emissions, noise, vibration and harshness testing (NVH). Different types of the vehicle attachment system in the dynamometer cell and its influences on the driving force characteristics and the vehicle acoustic signature is also discussed. The text also highlights the impact of the use of the chassis dynamometer as a development platform and its impact on the development process. Examples of using chassis dynamometer as a development platform using Vehicle Hardware In-the-Loop (VHiL) approach including drivability assessment and transmission calibrations are presented.

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.

This paper is a review of turbulence models and computational methods that have been produced at Clemson University's Advanced Computational Research Laboratory. The goal of the turbulence model development has been to create physics-based models that are economically feasible and can be used in a competitive environment, where turnaround time is a critical factor. Given this goal, all of the work has been focused on Reynolds-Averaged Navier-Stokes (RANS) simulations in the eddy-viscosity framework with the majority of the turbulence models having three transport equations in addition to mass, momentum, and energy. Several areas have been targeted for improvement in turbulence modeling for complex flows such as those found in motorsports aerodynamics: the effects of streamline curvature and rotation on the turbulence field, laminar-turbulent transition, and separated shear layer rollup and breakdown.

Understanding the effects of tire and vehicle properties on the rollover propensity of tractor semi-trailer trucks is essential. The major objective of the project described by this paper was to develop a simplified computational tool that can be used to understand and predict the effects of various tire characteristics and truck design parameters on rollover under steady cornering and non-tripped conditions. In particular, this tool may be used to help understand the basic mechanisms governing rollover propensity of trucks equipped with New Generation Wide Single tires as contrasted with conventional tires. Effects of tire flexibility, roll-compliant suspensions, fifth - wheel lash and nonlinear suspension characteristics are included in the model and are presented below. Design parameter data used as input to the model were obtained from Michelin Americas Research and Development Corporation.

Understanding the parameters which influence the tendency for a heavy truck to exhibit rollover is of paramount importance to the trucking industry. Multiple parameters influence the vehicle’s motion, and the ability to determine how each affects the vehicle as a system would be an indispensable tool for the design of such vehicles. To be able to perform such predictions and analysis, models and a computer simulation were created to allow the examination of changes in design parameters in such vehicles. The vehicle model was originally developed by Law [1] and presented in Law and Janajreh [2]. The model was extended further by Lawson [3, 4] to include (a) the effects of the torsional compliance of both the tractor and trailer, and (b) tanker trailers with various levels of liquid fill. In the present paper, both the tractor and trailer compliances were studied independently to determine their influences on the rollover stability of the vehicle.

Current military operations in Iraq and Afghanistan are unique because the battlefield can be described as a non-linear, asymmetrical environment. Units operate in zones that are susceptible to enemy contact from any direction at any time. The response to these issues has been the addition of add-on armor to HMMWV's and other tactical vehicles. The retro-fitting of armor to these vehicles has resulted in many accidents due to rollover and instability. The goal of this paper is to determine possible causes of the instability and rollover of up-armored tactical vehicles and to develop simulation tools that can analyze the steady-state and transient dynamics of the vehicles. Models and simulations include a steady-state rollover scenario, analysis of understeer gradient, and a transient handling analysis that uses models of both a human driver and a vehicle to analyze vehicle response to an obstacle avoidance maneuver.

Decreasing the propensity for rollover during steady state cornering of tractor semi-trailers is a key advantage to the trucking industry. This will be referred to as “increasing the lateral stability during steady state cornering” and may be accomplished by changes in design and loading variables which influence the behavior of a vehicle. To better understand the effects of such changes, a computer program was written to optimize certain design variables and thus maximize the lateral acceleration where an incipient loss of lateral stability occurs. The vehicle model used in the present investigation extends that developed by Law [1] and presented in Law and Janajreh [2]. The original model included the effects of tire flexibility, nonlinear roll-compliant suspensions, and fifth wheel lash. This model was modified to include (a) additional effects of displacement due to both lateral and vertical tire flexibility, and (b) provisions for determining “off-tracking”.

It’s important to predict human actions in the industry assembly process. Foreseeing future actions before they happened is an essential part for flexible human-robot collaboration and crucial to safety issues. Vision-based human action prediction from videos provides intuitive and adequate knowledge for many complex applications. This problem can be interpreted as deducing the next action of people from a short video clip. The history information needs to be considered to learn these relations among time steps for predicting the future steps. However, it is difficult to extract the history information and use it to infer the future situation with traditional methods. In this scenario, a model is needed to handle the spatial and temporal details stored in the past human motions and construct the future action based on limited accessible human demonstrations.

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.

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.

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.