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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 describes the design and application of a business simulation to help train employees about the new business model and culture that for an automotive supplier company that designs connected vehicle and other advanced electronic products for the automotive industry. The simulation, called SIM-i-TRI, is a three to four day collaborative learning activity that simulates the executive, administrative, engineering, manufacturing, and marketing functions in three divisions of a manufacturer that supplies parts and systems to customers in industries similar to the automotive industry. It was originally designed to support the new employee orientation at the Tier 1 supplier and to provide the participants a safe environment to practice the lessons from the orientation. The simulation has been used several times a month in the US, England, and Germany for over four years.

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.

Although computer models for vehicle and sub-system performance simulations have been developed and used extensively in the past several decades, there is currently a need to enhance the overall availability of these types of tools. Increasing demands on vehicle performance targets have intensified the need to obtain rapid feedback on the effects of vehicle modifications throughout the entire development cycle. At the same time, evolution of the PC and development of Web-based applications have contributed to the availability, accessibility, and user-friendliness of sophisticated computer analysis. Web engineering is an ideal approach in supporting globalization and is a cost-effective design-analysis integration business strategy. There is little doubt that this new approach will have positive impacts on product cost, quality, and development cycle time. This paper will show how Microsoft Excel and the Web can be powerful and effective tools in the development process.

This paper describes a case study led by Science Applications International Corporation (SAIC) of Dayton, OH USA and Dearborn Group Inc. to prove the feasibility of employing Telematics technologies to the vehicle test and measurement industry. Many test functions can be automated through the use of secure wireless technologies. For example, vehicle data can be dynamically monitored on the vehicle and data meeting pre-determined criteria could be downloaded via the wireless communications center. Additionally, central, real-time wireless monitoring of vehicle fleets provides the vehicle fleet manager with the ability to manage multiple tests simultaneously, thus improving efficiencies and potentially reducing manpower costs and compressing test schedules.

Ethanol fuel has received renewed attention in recent years because of its oxygenate content and its potential to reduce greenhouse gas emissions from spark ignition engines. The economic impact on farm industry has been one of the drivers for its use in engines in the U.S. Although ethanol, in various blends, has been used in automotive engines for almost a decade the fuel has seldom been utilized in off-highway engines where the fuel systems are not well controlled. This investigation was conducted to evaluate exhaust emissions and combustion characteristics of E85 fuel in an off-highway engine used in farm equipment. A single-cylinder, four-stroke, spark ignition engine equipped with a carburetor was used to investigate combustion and exhaust emissions produced by gasoline and blends of gasoline and ethanol fuels. The engine fuel system was modified to handle flow rates required by the engine. A variable size-metering orifice was used to control air-to-fuel ratios.

With the increasing presence of the Internet in today's applications, ranging from legacy enterprise systems to handheld devices and home appliances, there is an increasing need for a generic middleware that can enable the interoperability between heterogeneous systems. In the specific case of in-vehicle networks, a generic middleware would enable the vehicle/driver to interact with a diversity of applications, including legacy enterprise systems, other embedded systems and wireless ad-hoc and ubiquitous applications. This paper is the result of an initial investigation into the requirements for such a middleware, and possible directions to be taken for its implementation.

Nowadays, the automotive industry is experiencing the advent of unprecedented applications with connected devices, such as identifying safe users for insurance companies or assessing vehicle health. To enable such applications, driving behavior data are collected from vehicles and provided to third parties (e.g., insurance firms, car sharing businesses, healthcare providers). In the new wave of IoT (Internet of Things), driving statistics and users’ data generated from wearable devices can be exploited to better assess driving behaviors and construct driver models. We propose a framework for securely collecting data from multiple sources (e.g., vehicles and brought-in devices) and integrating them in the cloud to enable next-generation services with guaranteed user privacy protection.

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.

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.

Controller Area Network (CAN), the de facto standard for in-vehicle networks, has insufficient security features and thus is inherently vulnerable to various attacks. To protect CAN bus from attacks, intrusion detection systems (IDSs) based on advanced deep learning methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), have been proposed to detect intrusions. However, those models generally introduce high latency, require considerable memory space, and often result in high energy consumption. To accelerate intrusion detection and also reduce memory requests, we exploit the use of Binarized Neural Network (BNN) and hardware-based acceleration for intrusion detection in in-vehicle networks. As BNN uses binary values for activations and weights rather than full precision values, it usually results in faster computation, smaller memory cost, and lower energy consumption than full precision models.

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 article solves the problem of increasing the energy efficiency of the traction electric drive in the low load conditions. The set objective is achieved by analogy with internal combustion engines by decreasing the consumed energy using the amplitude control of the three-phase voltage of the induction machine. The basis of the amplitude control is laid by the constancy criterion of the overload capacity with respect to the electromagnetic torque, which provides a reliable reserve from a "breakdown" of the induction machine mode in a wide range of speeds and loads. The control system of the traction electric drive contains a reference model of electromechanical energy conversion represented by the generalized equations of the instantaneous balance of the active and reactive power and the mechanical load. The induction machine is controlled by two adaptive variables: the electromagnetic torque and the voltage amplitude.

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.

The engine operating point (EOP), which is determined by the engine speed and torque, is an important part of a vehicle's powertrain performance and it impacts FC, available propulsion power, and emissions. Predicting instantaneous EOP in real-time subject to dynamic driver behaviour and environmental conditions is a challenging problem, and in existing literature, engine performance is predicted based on internal powertrain parameters. However, a driver cannot directly influence these internal parameters in real-time and can only accommodate changes in driving behaviour and cabin temperature. It would be beneficial to develop a direct relationship between the vehicle-level parameters that a driver could influence in real-time, and the instantaneous EOP. Such a relationship can be exploited to dynamically optimize engine performance.