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Adaptive Cruise Control (ACC) is becoming a common feature in modern day vehicles with the advancement of Advanced Driver Assist Systems (ADAS). Simultaneously, Hardware-in-the-Loop (HIL) simulation has emerged as a major component of the automotive product development cycle as it can accelerate product development and validation by supplementing in-vehicle testing. Specifically, HIL simulation has become an integral part of the controls development and validation V-cycles by enabling rapid prototyping of control software for Electronic Control Units (ECUs). Traditionally, ACC algorithms have been validated on a system or subsystem HIL bench with the ACC ECU in the loop such that the HIL bench acts as the host or trailing vehicle with the target or preceding vehicle usually simulated using as an object that follows a pre-defined motion profile.

We present research in progress to develop and implement a transportable instrumentation package (TIP) to collect driver data in a vehicle. The overall objective of the project is to investigate the symbiotic relationship between humans and their vehicles. We first describe the state-of-art technologies to build the components of TIP that meet the criteria of ease of installation, minimal interference with driving, and sufficient signals to monitor driver state and condition. This method is a viable alternative to current practice which is to first develop a fully instrumented test vehicle, often at great expense, and use it to collect data from each participant as he/she drives a prescribed route. Another practice, as for example currently being used in the SHRP-2 naturalistic driving study, is to install the appropriate instrumentation for data collection in each individual's vehicle, often requiring several hours.

This paper describes a computerized value analysis tool (VAT) developed to aid automotive interior designers, engineers and planners to achieve the high levels of perceived quality of materials used in automotive door trim panels. The model requires a number of inputs related to types of materials, their manufacturing processes and customer perceived quality ratings, costs and importance of materials, features located in different areas of the door trim panel, etc. It allows the user to conduct iterative evaluation of total cost, total weighted customer perceived quality ratings, and estimates of perceived value (perceived quality divided by cost) for different door trim areas as well as the entire door trim panel. The VAT, thus, allows value and cost management related to materials and processing choices for automotive interiors.

Position of the H-point plays a vital role during designing the seating system. The seating system provides support and comfort to the occupants while they are operating the vehicle. The traditional way to design a seat system is to use rules of thumb and experience, which often results in several costly design iterations. The purpose of this paper is to demonstrate the capability of CAE analytical tool to find the H-point at the early phase of the seating system design without compromising the comfort level of the occupant. The recently launched Lincoln Continental front seating system was used to validate this purpose. The Continental seating system has unique design features which provide special challenges in designing and simulating the seat. With the help of CAE analytical tool, the traditional process is streamlined and a seat design could be achieved in a shorter period with greater accuracy.

The rapid development of connected and automated vehicle technologies together with cloud-based mobility services are revolutionizing the transportation industry. As a result, huge amounts of data are being generated, collected, and utilized, hence providing tremendous business opportunities. However, this big data poses serious challenges mainly in terms of data privacy. The risks of privacy leakage are amplified by the information sharing nature of emerging mobility services and the recent advances in data analytics. In this paper, we provide an overview of the connected vehicle landscape and point out potential privacy threats. We demonstrate two of the risks, namely additional individual information inference and user de-anonymization, through concrete attack designs. We also propose corresponding countermeasures to defend against such privacy attacks. We evaluate the feasibility of such attacks and our defense strategies using real world vehicular data.

This paper presents a comparison of aqueous corrosion rates in 5% NaCl solution for eight experimental creep-resistant magnesium alloys considered for automotive powertrain applications, as well as three reference alloys (pure magnesium, AM50B and AZ91D). The corrosion rates were measured using the techniques of titration, weight loss, hydrogen evolution, and DC polarization. The corrosion rates measured by these techniques are compared with each other as well as with those obtained with salt-spray testing using ASTM B117. The advantages and disadvantages of the various corrosion measurement techniques are discussed.

This paper presents results of a study conducted to compare driving behavior and performance of drivers in two different fixed-base driving simulators (namely, FAAC and STI) while performing a same set of distracting tasks under geometrically similar freeway and traffic conditions. The FAAC simulator had a wider three-screen road view with steering feedback as compared to the STI simulator which had a single screen and narrower road view and had no steering feedback. Twenty four subjects (12 younger and 12 mature) drove each simulator and were asked to perform a set of nine different tasks involving different distracting elements such as, using a cell phone, operating the car radio, retrieving and selecting a map from map pocket in the driver's door, collecting coins to pay toll, etc.

This paper presents results of a three-phase research project aimed at understanding how future automotive interior materials should be selected or designed to satisfy the needs of the customers. The first project phase involved development of 22 five-point semantic differential scales to measure visual, visual-tactile, and evaluative characteristics of the materials. Some examples of the adjective pairs used to create the semantic differential scales to measure the perceptual characteristics of the material are: a) Visual: Light vs. Dark, Flat vs. Shiny, etc., b) Visual-Tactile: Smooth vs. Rough, Slippery vs. Sticky, Compressive vs. Non-Compressive, Textured vs. Non-Textured, etc., c) Evaluative (overall perception): Dislike vs. Like, Fake vs. Genuine, Cheap vs. Expensive, etc. In the second phase, 12 younger and 12 older drivers were asked to evaluate a number of different automotive interior materials by using the 22 semantic differential scales.

This paper presents results of a research project conducted to develop a methodology and to refine the specifications of a small, low mass, low cost vehicle being developed at the University of Michigan-Dearborn. The challenge was to assure that the design would meet the needs and expectations of customers in three different countries, namely, China, India and the United States. U.S, Chinese and Indian students studying on the university campus represented customers from their respective countries for our surveys and provided us with the necessary data on: 1) Importance of various vehicle level attributes to the entry level small car customer, 2) Preferences to various features, and 3) Direction magnitude estimation on parameters to size the vehicle for each of the three markets.

This paper describes a research project currently in-progress to develop a parametric model of a vehicle for use in early design stages of a new vehicle program. The model requires key input parameters to define the kind of new vehicle to be designed — in terms of details such as its intended driver/user population, vehicle type (e.g. 2-box, 3-box designs), and some key exterior and interior dimensions related to its size and proportions. The model computes and graphically displays interior package, ergonomics zones for driver controls and displays, and field of views through window openings. It also allows importing or inputting and superimposing and manipulating exterior surfaces created by a designer to assess compatibility between the interior occupant package and the vehicle exterior.

To meet legal requirements vehicle manufacturers have to use a standard drive-by noise acceleration test conforming to relatively easily specified procedures (gear, approach speed etc). However, due to the transient conditions occurring during the test, predicting maximum drive-by noise levels from the contributions of vehicle systems is difficult. As manufacturers need to identify early in the design of a vehicle those available systems which will ensure legal requirements are met, a technique is required that can predict the contribution of each system. The technique has to be able to accept system target & CAE data as well as test data in order that it can be used in all stages of a vehicle program.

As intelligent automated vehicle technologies evolve, there is a greater need to understand and define the role of the human user, whether completely hands-off (L5) or partly hands-on. At all levels of automation, the human occupant may feel anxious or ill-at-ease. This may reflect as higher stress/workload. The study in this paper further refines how perceived workload may be determined based on occupant physiological measures. Because of great variation in individual personalities, age, driving experiences, gender, etc., a generic model applicable to all could not be developed. Rather, individual workload models that used physiological and vehicle measures were developed.

This paper discusses the evolution of graduate education in manufacturing engineering and the curriculum needed to educate manufacturing engineers in the automotive industry. This paper examines the master's and doctoral curriculum in manufacturing engineering at the University of Michigan-Dearborn. Finally, it proposes future direction for graduate education in manufacturing that will be needed for the automotive industry of the future.

The automotive industry is expected to accelerate the transition to revolutionary products, rapid changes in technology and increasing technological sophistication. This will require engineers to advance their knowledge, connect and integrate different areas of knowledge and be skilled in synthesis. In addition, they must learn to work in cross-disciplinary teams and adopt a systems approach. The College of Engineering and Computer Science (CECS) at the University of Michigan-Dearborn (UM-Dearborn) responded by creating interdisciplinary MS and Ph.D. programs in automotive systems engineering (ASE) and augmenting them with hands-on research. Students at the undergraduate level can also engage in numerous ASE activities. UM-Dearborn's ASE programs offer interesting and possibly unique advantages. The first is that it offers a spectrum of ASE degree and credit programs, from the MS to the Ph.D. to continuing education.

With recent advances in microprocessors and data storage technologies, vehicle users can now bring or access large amounts of data in vehicles for purposes such as communication (e.g. e-mail, phone books), entertainment (e.g. music and video files), browsing and searching for information (e.g. on-board computers and internet). The challenge for the vehicle designer is how to design data displays and retrieval methods to allow data search and manipulation tasks by managing driver workload at safe acceptable levels. This paper presents a data retrieval menu system developed to assess levels of screens (depth of menu) that may be needed to select required information when a vehicle is equipped with the capability to access audio files, cell phone, PDA, e-mail and “On-star” type functions.

The sophistication of all-wheel-drive technology is approaching the point where the drive torque to each wheel can be independently controlled. This potentially offers vehicle handling enhancements similar to those provided by Dynamic Stability Control, but without the inevitable reduction in vehicle acceleration. Independent control of all-wheel-drive torque distribution would therefore be especially beneficial under acceleration close to the limit of stability. A vehicle model of a typical sports sedan was developed in Simulink, with fully independent control of torque distribution. Box-Behnken experimental design was employed to determine which torque distribution parameters have the greatest impact on the vehicle course and acceleration. A proportional-integral control strategy was implemented, applying yaw rate feedback to vary the front-rear torque distribution, and lateral acceleration feedback to adjust the left-right distribution.

Yaw and roll stability limits are derived for three quasi-static roll plane models: rigid vehicle, suspended vehicle, and compliant tire vehicle. A generalized stability equation is identified that fits the stability limits for each model. This generalized stability equation leads to the definition of two new parameters referred to as the generalized superelevation and generalized center of gravity height. These parameters are shown to be physically meaningful. The use of linearizing assumptions is minimized and road superelevation is included, resulting in a more complete equation for each stability limit. Each derived stability limit is then compared and contrasted to the typical representations found in the literature.

Autonomous vehicles and advanced driver assistance systems have functionality realized across numerous distributed systems that interact with a dynamic cyber-physical environment. This complexity raises the potential for emergent behaviours which are not intended for the system’s operational use. The need to analyze the intended functionality of these emergent behaviours for potential hazards, which may occur in absence of faults, are aspects of the ISO PAS 21448, Safety of the Intended Functionality (SOTIF) [1]. The Safety of the Intended Functionality or SOTIF is a framework for developing systems which are free from unreasonable risk due to the intended functionality or performance limitations of a system which is free from faults. This is meant to complement Functional Safety which is covered in ISO 26262 [2]. The major focus of SOTIF is to aid in the functional development of a system.

LS-DYNA3D has been widely used to perform computer simulation of sheet metal forming. In the material library of LS-DYNA3D there are a number of user defined material models. In order to take full advantage of the material subroutines, it is important for the users to be able to display user defined history variables in the post processing and to establish user-defined failure criterion. In this report, the development of a damage coupled plastic model is firstly described. The damage model is then programmed in a user defined material subroutine. This is followed by performing finite element simulation of sheet metal forming with the LS-DYNA3D that has incorporated the damage coupled plastic model. The way to display the user defined history variables and how to deal with the failure criterion during the postprocessing of ETA/DYNAFORM are described. History variable distributions at several time steps are displayed and discussed in this paper.

Recommender systems guide a user to useful objects in a large space of possible options in a personalized way. In this paper, we study recommender systems for vehicles. Compared to previous research on recommender systems in other domains (e.g., movies or music), there are two major challenges associated with recommending vehicles. First, typical customers purchase fewer cars than movies or pieces of music. Thus, it is difficult to obtain rich information about a customer’s vehicle purchase history. Second, content information obtained about a customer (e.g., demographics, vehicle preferences, etc.) is also difficult to acquire during a relatively short stay in a dealership. To address these two challenges, we propose an interactive vehicle recommender system based a novel machine learning method that integrates decision trees and multi-armed bandits. Decision tree learning effectively selects important questions to ask the customer and encodes the customer's key preferences.