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This paper compares the results from three human factors studies conducted in a motion-based simulator in 2008, 2014 and 2023, to highlight the trends in driver's response to Forward Collision Warning (FCW). The studies were motivated by the goal to develop an effective HMI (Human-Machine Interface) strategy that enables the required driver's response to FCW while minimizing the level of annoyance of the feature. All three studies evaluated driver response to a baseline-FCW and no-FCW conditions. Additionally, the 2023 study included two modified FCW chime variants: a softer FCW chime and a fading FCW chime. Sixteen (16) participants, balanced for gender and age, were tested for each group in all iterations of the studies. The participants drove in a high-fidelity simulator with a visual distraction task (number reading). After driving 15 minutes in a nighttime rural highway environment, a surprise forward collision threat arose during the distraction task.

The automotive industry is passing for a big transformation, due to technologies advance. The electrical technologies are also on a good rising curve, calling the attention of the Original Equipment Manufacturer (OEMs). This scenario generates the demand for a faster method to train their new hired engineers, when compared with usual on the job training. Model in the Loop (MiL) consists in one of the real-time embedded systems test phases, which is developed in a computational environment, performing a mathematical modeling of the system, presenting an interface that allows the visualization of its dynamics and the signals involved. Two powerful software in industry that apply MiL are the Matlab and Simulink. A project involving these applications was proposed for a team of new hired engineers, developing models of several vehicle Electronic Control Units (ECUs), with some scope reduction as an example the functional requirements reduction.

This paper focuses on finding and analyzing the relevant parameters affecting traffic flow when autonomous vehicles are introduced for ride hailing applications and autonomous shuttles are introduced for circulator applications in geo-fenced urban areas. For this purpose, different scenarios have been created in traffic simulation software that model the different levels of autonomy, traffic density, routes, and other traffic elements. Similarly, software that specializes in vehicle dynamics, physical limitations, and vehicle control has been used to closely simulate realistic autonomous vehicle behavior under such scenarios. Different simulation tools for realistic autonomous vehicle simulation and traffic simulation have been merged together in this paper, creating a realistic simulator with Hardware-in-the-Loop (HiL), Traffic-in-the-Loop (TiL), and Software in-the-Loop (SiL) simulation capabilities.

Wheel slip control is crucial to active safety control systems such as Traction Control System (TCS) and Anti-lock Braking System (ABS) that ensure vehicle safety by maintaining the wheel slip in a stable region. For this reason, a wide variety of control methods has been implemented by both researchers and in the industry. Moreover, the use of new electro-hydraulic or electro-mechanical brakes, and in-wheel electric motors allow for a more precise wheel slip control, which should further improve the vehicle dynamics and safety. In this paper, we compare two methods for wheel slip control: a loop-shaping Youla parametrization method, and a sliding mode control method. Each controller is designed based on a simple single wheel system. The benefits and drawbacks of both methods are addressed. Finally, the performance and stability robustness of each controller is evaluated based on several metrics in a simulation using a high-fidelity vehicle model with several driving scenarios.

Real-world second-by-second vehicle driving cycle data is very important for vehicle research and development. A project solely dedicated to generating such information would be tremendously costly and time consuming. Alternatively, we developed such a database by utilizing two publicly available passenger vehicle travel surveys: 2004-2006 Puget Sound Regional Council (PSRC) Travel Survey and 2011 Atlanta Regional Commission (ARC) Travel Survey. The surveys complement each other - the former is in low time resolution but covers driver operation for over one year whereas the latter is in high time resolution but represents only one-week-long driving operation. After analyzing the PSRC survey, we chose 382 vehicles, each of which continuously operated for one year, and matched their trips to all the ARC trips. The matching is carried out based on trip distance first, then on average speed, and finally on duration.

The fuel consumption of a vehicle is shown to be linearly proportional to (1) total vehicle work required to drive the cycle due to mass and acceleration, tire friction, and aerodynamic drag and (2) the powertrain (PT) mechanical losses, which are approximately proportional to the engine displaced volume per unit distance travelled (displacement time gearing). The fuel usage increases linearly with work and displacement over a wide range of applications, and the rate of increase is inversely proportional to the marginal efficiency of the engine. The theoretical basis for these predictions is reviewed. Examples from current applications are discussed, where a single PT is used across several vehicles. A full vehicle cycle simulation model also predicts a linear relationship between fuel consumption, vehicle work, and displacement time gearing and agrees well with the application data.

Electric vehicles are highly sensitive to variations in environmental factors (like temperature, drive style, grade, etc.). The distribution of real-world range of electric vehicles due to these environmental factors is an important consideration in target setting. This distribution can be obtained by running several simulations of an electric vehicle for a number of high-frequency velocity, grade, and temperature real-world trip profiles. However, in order to speed up simulation time, a unique set of drive profiles that represent the entire real-world data set needs to be developed. In this study, we consider 40,000 unique velocity and grade profiles from various real-world applications in EU. We generate metadata that describes these profiles using trip descriptor variables. Due to the large number of descriptor variables when considering second order effects, we normalize each descriptor and use principal component analysis to reduce the dimensions of our dataset to six components.

The initiative of this paper is focused on improving the heat dissipation from the pressure wheel of a laser welding assembly in order to achieve a longer period of use. The work examines the effects of different geometrical designs on the thermal performance of pressure wheel assembly during a period of cooling time. Three disc designs were manufactured for testing: Design 1 – a plain wheel, Design 2 – a pierced wheel, and Design 3 – a wheel with ventilating vanes. All of the wheels were made of carbon steel. The transient thermal reaction were compared. The experimental results indicate that the ventilated wheel cools down faster with the convection in the ventilated channels, while the solid plain wheel continues to possess higher temperatures. A comparison among the three different designs indicates that the Design 3 has the best cooling performance.

There has been a general consensus in the scientific literature that a rim gouging, not scraping, into a roadway surface generates very high forces which can cause a vehicle to overturn in some situations. However, a paper published in 2004 attempts to minimize the forces created during wheel rim gouging and the effect on vehicle rollover. This paper relied largely on heavily filtered lateral acceleration data and discounted additional test runs by the authors and NHTSA that did not support the supposed conclusions. This paper will discuss the effect of rim gouging using accepted scientific methods, including full vehicle testing where vehicle accelerations were measured during actual rim gouging events and static testing of side forces exerted by wheels mounted on a moving test fixture. The data analyzed in this paper clearly shows that forces created by rim gouges on pavement can be thousands of Newtons and can contribute to vehicle rollover.

Not only well-functioning, but also the way operating everyday items "feel", gauges costumer perception of an automobile robustness. To prevent costumer dissatisfaction with door trim panel movement when operating power windows, deflections must be kept small. Deflections of inner panel are seen through trim panel and are responsible for giving a flimsy idea of the door. In this paper, inner panel movement for a fully stamped door in full glass stall up position is analyzed. Through CAE analyses, inner panel behavior was compared, considering different types of reinforcement for belt region.

At various milestones during a vehicle’s development program, different CAE models are created to assess NVH error states of concern. Moreover, these CAE models may be developed in different commercial CAE software packages, each one with its own unique advantages and strengths. Fortunately, due to the wide spread acceptance that the Functional Mock-up Interface (FMI) standard gained in the CAE community over the past few years, many commercial CAE software now support cosimulation in one form or the other. Cosimulation allows performing multi-domain/multi-resolution simulations of the vehicle, thereby combining the advantages of various modeling techniques and software. In this paper, we explore cosimulation of full 3D vehicle model developed in MSC ADAMS with 1D driveline model developed in LMS AMESim. The target application of this work is investigation of vehicle NVH error states associated with both hybridized and non-hybridized powertrains.

Modeling of High Voltage (HV) wires is an important aspect of vehicle safety simulations for electrified powertrains to understand the potential tearing of the wire sheath or pinching of HV wiring. The behavior of the HV wires must be reviewed in safety simulations to identify potential hazards associated with HV wire being exposed, severed, or in contact with ground planes during a crash event. Modeling HV wire is challenging due to the complexity of the physical composition of the wire, which is usually comprised of multiple strands bundled and often twisted together to form the HV electrical conductor. This is further complicated by the existence of external insulating sheathing materials to prevent HV exposure during normal operating conditions. This paper describes a proposed method to model and characterize different types of HV wires for usage in component- and vehicle-level safety models.

Customer expectations for improved performance, comfort levels, and aesthetics have led automobile manufacturers to use leather for seats, steering wheels, instrument panels, door panels, and other components. To increase the drivers’ comfort level, there is always a soft pad layer applied under the leather in the steering wheel. This paper will describe a potential failure mode that occurs when materials migrate from one material to another material in multilayer material constructions. In this case dioctyl phthalate migrated from the soft pad layer into the leather surface, affecting the durability performance of the leather coating. This paper describes the failure and demonstrates an effective test methodology to test for this failure during the materials and components validation process.

The potential for Augmented Reality (AR) spans many domains. Among other applications, AR can improve the discovery and learning experience for users inspecting a particular item. This paper discusses the use of AR in the automotive context; particularly, on improving the user experience in a dealership show room. Visual augmentation, through a tablet computer or glasses allows users to take part in a self-guided tour in learning about the various features, details, and options associated with a vehicle. The same approach can be applied to other learning scenarios, such as training and maintenance assistance. We evaluated a set of AR Glasses and a general purpose tablet. A table-top showroom was developed demonstrating what the actual user experience would be like for a self-guided dealership tour using natural markers and three-dimensional content spatially registered to physical objects in the user’s field of view.

This paper presents an advanced yaw stability control system that uses a sensor set including an inertial measurement unit to sense the 6 degrees-of-freedom motions of a vehicle. The full degree of the inertial measurement unit improves and enhances the vehicle motion state estimation over the one in the traditional electronic stability controls. The addition of vehicle state estimation leads to the performance refinement of vehicle stability control that can improve performance in certain situations. The paper provides both detailed system description and test results showing the effectiveness of the system.

Software for autonomous vehicles is highly complex and requires vast amount of vehicle testing to achieve a certain level of confidence in safety, quality and reliability. According to the RAND Corporation, a 100 vehicle fleet running 24 hours a day 365 days a year at a speed of 40 km/hr, would require 17 billion driven kilometers of testing and take 518 years to fully validate the software with 95% confidence such that its failure rate would be 20% better than the current human driver fatality rate [1]. In order to reduce cost and time to accelerate autonomous software development, Hardware-in-the-Loop (HIL) simulation is used to supplement vehicle testing. For autonomous vehicles, path following controls are an integral part for achieving lateral control. Combining the aforementioned concepts, this paper focuses on a real-time implementation of a path-following lateral controller, developed by Freund and Mayr [2].

This paper proposes a method to make diagnostic/prognostic judgment about the health of a tire, in term of its wear, using existing on-board sensor signals. The approach focuses on using an estimate of the effective rolling radius (ERR) for individual tires as one of the main diagnostic/prognostic means and it determines if a tire has significant wear and how long it can be safely driven before tire rotation or tire replacement are required. The ERR is determined from the combination of wheel speed sensor (WSS), Global Positioning sensor (GPS), the other motion sensor signals, together with the radius kinematic model of a rolling tire. The ERR estimation fits the relevant signals to a linear model and utilizes the relationship revealed in the magic formula tire model. The ERR can then be related to multiple sources of uncertainties such as the tire inflation pressure, tire loading changes, and tire wear.

Use of sensors to monitor dynamic performance of machine tools at Ford’s powertrain machining plants has proven to be effective. The traditional approach to convert sensor data to actionable intelligence consists of identifying single features from cycle based signatures and setting thresholds above acceptable performance limits based on trials. The thresholds are used to discriminate between acceptable and unacceptable performance during each cycle and raise alarms if necessary. This approach requires a significant amount of resource & time intensive set up work up-front and considerable trial and error adjustments. The current state does not leverage patterns that might be discernible using multiple features simultaneously. This paper describes enhanced methods for processing the data using supervised and unsupervised machine learning methods. The objective of using these methods is to improve the prediction accuracy and reduce up-front set up.

Along with the development and marketability of vehicles without an internal combustion engine, electrically heated surfaces within these vehicles are getting more and more important. They tend to have a quicker response while using less energy than a conventional electric heater fan, providing a comfortable temperature feel within the cabin. Due to the big area of heated surface it is important to spread the heating power in a way that different heat conduction effects to underlying materials are considered. In case an accurate sensor feedback of the targeted homogeneous surface temperature cannot be guaranteed, a thermal energy model of the heated system can help to set and maintain a comfortable surface temperature. For a heated steering wheel development project, different models have been created to meet that aim using mechanistic approaches starting with a predominantly first-order dynamics model and ending with a distributed parameter multi-feedback system.

Halfshafts are very important components from vehicle powertrain. They are the element responsible to transmit torque and rotation from transmission to wheels. Its most basic design consists of a solid bar with joints at each extreme. Depending of bar length, the natural frequency of first bending mode might have a modal alignment with engine second order, resulting in undesired noise on vehicle interior. Many design alternatives are available to overpass this particular situation, like adding dampers, use tube shafts or use link-shafts, however, all of them are cost affected. This study proposes an investigation to obtain an optimal profile for a solid shaft, pursuing the lowest possible frequency for the first bending mode by changing its diameter at specific regions. The study is divided in four main stages: initially, a modal analysis of a halfshaft is done at vehicle to determinate its natural frequency when assembled on vehicle.