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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.

In the U.S., the teenage driving population is at the highest risk of being involved in a crash. Teens often demonstrate poor vehicle control skills and poor ability to identify hazards, thus proper understanding of automotive indicators and warnings may be even more critical for this population. This research evaluates teen drivers’, between 15 to 17 years of age, understanding of symbols from vehicles featuring advanced driving assistant systems and multiple powertrain configurations. Teen drivers’ (N=72) understanding of automotive symbols was compared to three other groups with specialized driving experience and technical knowledge: automotive engineering graduate students (N=48), driver rehabilitation specialists (N=16), and performance driving instructors (N=15). Participants matched 42 symbols to their descriptions and then selected the five symbols they considered most important.

The emphasis on vehicle fuel economy and tailpipe emissions, coupled with a trend toward greater system functionally, has prompted automotive engineers to develop on-board control systems with increased requirements and complexity. Mainstream engine controllers regulate fuel, spark, and other subsystems using custom solutions that incorporate off-the-shelf hardware components. Although the digital processor core and the peripheral electronics may be similar, these controllers are targeted to fixed engine architectures which limit their flexibility across vehicle platforms. Moreover, additional software needs are emerging as electronics continue to permeate the ground transportation sector. Thus, automotive controllers will be required to assume increased responsibility while effectively communicating with distributed hardware modules.

This paper proposes a conceptual design tool that could direct designers towards concepts that lead to reduced gaps on the exterior of an automobile. Apart from the manufacturing and assembly tolerance stack up, the design and integration method of the body panels in an automobile contribute to the gap. . A benchmark study suggested cursory concepts to avoid or minimize the gaps. The proposed design tool uses a modified morphological chart approach to populate a table with concepts obtained from the benchmark study and by other means. The design tool also incorporates decision alternatives and hence is different from a morphological chart. The design tool can be used to highlight the occurrence of a high level tolerance stack up chain on the structural/mounting members. Conceptual component architectures are arranged in such a fashion to facilitate combinations through visual means.

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.

Race teams compete at a level where fractions of a second separate the finishers. Consequently, teams devote significant resources to gain a competitive edge. Limitations on track time and high track rental prices dictate efficiency in testing. Thus, proper use of data acquisition and computer aided engineering tools is essential. These tools can be used to quickly analyze test data and serve as the basis for recommendations for changes in chassis setup and driver technique. This project describes the further development of such a tool that can be used to analyze and diagnose the control inputs of a driver as well as diagnose the overall balance of the chassis (i.e., understeer and oversteer). This tool is an “expert system” (implemented in MATLAB) that provides an understanding of the effects of both chassis setup changes and driver steering, braking, and throttle control inputs on overall lap times.

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.

An investigation of the vertical dynamics of a tractor semi-trailer traversing a random road profile was conducted. This paper presents the development of a 14 degree-of-freedom (DOF), dynamic ride model of a tractor semi-trailer. It is based on work previously conducted by Vaduri and Law [1] and Law et al [2]. The DOFs include: (a) vertical displacements of each of the five axles, the tractor frame, the engine on its mounts, the cab on its suspension, and the driver's seat; (b) pitch displacements of the trailer with respect to the tractor, the cab, and the rigid tractor frame; and, (c) the first bending or beaming modes of the tractor and trailer frames. The model also incorporates suspension friction, and tire non-uniformities. The simulation of the model is conducted using MATLAB software.

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.

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.

Tradespace exploration (TSE) is an important aspect of the early stages of the design process, in which stakeholders search for the most optimal solutions within a design variable-bounded solution space. This decision-making process requires stakeholders to understand the trade-offs and compromises that may be required to choose a solution. In order for stakeholders to make these decisions appropriately, information must be presented in an efficient manner and should ensure that the trade-offs between solutions are clearly visible. Existing visualizations often struggle to elucidate these trade-offs, and can rapidly become difficult to understand as the dimensionality of the tradespace increases. In this paper, the benefits and drawbacks to these existing methods will be discussed. In addition, this paper will explore potential methods to improve information presentation for TSE, including framing, visual steering, and visualization options.

The ability to independently transfer into and out of a vehicle is essential for many wheelchair users to achieve driving independence. The purpose of the current study is to build upon the previous exploratory study that investigated the transfer strategies of wheelchair users by observing YouTube videos. This observational study videotaped five wheelchair users transferring from their wheelchairs into two research vehicles, a small and mid-size sedan that were equipped with a 50mm grid. The goal of this study was to use these videos and vehicle grids to precisely identify ingress and egress motions as well as “touch points” in a controlled setting with a small sample of five male wheelchair users. Using the videos from multiple different camera perspectives, the participants’ ingress and egress transfers were coded, documenting the touch points and step-by-step action sequences.

In this paper, we present a framework for the robot to learn how to place objects to a workpiece by learning from humans in smart manufacturing. In the proposed framework, the rational scene dictionary (RSD) corresponding to the keyframes of task (KFT) are used to identify the general object-action-location relationships. The Generalized Voronoi Diagrams (GVD) based contour is used to determine the relative position and orientation between the object and the corresponding workpiece at the final state. In the learning phase, we keep tracking the image segments in the human demonstration. For the moment when a spatial relation of some segments are changed in a discontinuous way, the state changes are recorded by the RSD. KFT is abstracted after traversing and searching in RSD, while the relative position and orientation of the object and the corresponding mount are presented by GVD-based contours for the keyframes.

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.

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.

This paper describes the development and simulation of a voice and pointing gesture interaction system for on-route update of autonomous vehicles’ path. The objective of this research is to provide users of autonomous vehicles a human vehicle interaction mode that enables them to make and communicate spontaneous decisions to the autonomous car, modifying its pre-defined autonomous route in real-time. For example, similar to giving directions to a taxi driver, a user will be able to tell the car «Stop there» or «Take that exit». In this way, the user control/spontaneity vs interaction flexibility dilemma that current autonomous vehicle concepts have, could be solved, potentially increasing the user acceptance of this technology. The system was designed following a level structured state machine approach. The simulations were developed using MATLAB and VREP, a robotics simulation platform, which has accurate vehicle and sensor models.

Compared to traditional materials, carbon fiber reinforced polymers (CRFPs) have allowed designers to design stiff, light-weight structures, but at the cost of increased complexity in the design process. In this paper, the automation and optimization of the composite design process and how it affects design space exploration are evaluated. Specifically investigated is the design process for CFRP sandwich structures using the third-party optimization software modeFRONTIER. For given surface geometry and load cases, the approach aims to explore the Pareto frontier for the minimization of mass while constraining stiffness parameters. In this approach, the problem is framed as a single integrated optimization problem. In each optimization iteration, this method updates the CAD geometry and discretization of plies across the structure before exporting the model for Finite Element Analysis (FEA).

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.

Since 1989, ISO has published procedures for developing and testing public information symbols (ISO 9186), while the SAE standard for in-vehicle icon comprehension testing (SAE J2830) was first published in 2008. Neither testing method was designed to evaluate the comprehension of symbols in modern vehicles that offer digital instrument cluster interfaces that afford new levels of flexibility to further improve drivers’ understanding of symbols. Using a driving simulator equipped with an eye tracker, this study investigated drivers’ understanding of six automotive symbols presented on in-vehicle displays. Participants included 24 teens, 24 adults, and 24 senior drivers. Symbols were presented in a symbol-only, symbol + short text descriptions, and symbol + long text description conditions. Participants’ symbol comprehension, driving performance, reaction times, and eye glance times were measured.

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.