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Modular systems provide the ability to achieve product variety through the combination and standardization of components. The trend within the automotive industry is towards modular systems. The automotive manufacturers separate the vehicle into modular systems (chunks), which may be built and tested off line before assembled for vehicle installation. In this paper, software that utilizes a new clustering algorithm for development of integrated and modular vehicle architectures is presented. The algorithm and software tool presented in this paper allows the system designer to automate and optimize the vehicle system development process. A vehicle system model that identifies all the functional (data, and control) interfaces between vehicle system functions is the input to the software tool. The software tool is capable of analyzing this input data and determining the vehicle system architecture by optimally grouping (integrating) functions into physical modules.

This paper presents results of a study conducted to apply and evaluate the In-Vehicle Information System (IVIS) DEMAnD Model developed recently by the Virginia Polytechnic University's Center for Transportation Research for the Federal Highway Administration. This software-based model allows vehicle design engineers to predict the effects an in-vehicle information system might have on driver performance. The model was exercised under nine different driver attention task levels ranging from simple, such as glancing into a side view mirror, to complex, such as operating an in-vehicle navigation system. The nine driver tasks were evaluated using three different vehicle configurations and two levels of driver-roadway complexity. In addition, real-world information on driver visual performance was also collected during four different tasks for comparison with model predictions of these same functions.

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.

Not all software tools are created equal and not all software tools are created to perform the same tasks. Therefore, different software tools are used to perform different tasks. However, being able to share the information between the different software tools, without having to manually re-enter (duplicate) any of the information, can save a lot of time and improve the quality of the product. The control software interface presented in this paper, allows system engineers to exchange data between software tools in an efficient manner which maximizes each tools capabilities and ultimately reduces development time and improves the quality of the product.

This paper documents the design and verification process of a modular spaceframe platform for SUV, hatchback, sedan and station wagon conducted at the Institute for Advanced Vehicle Systems (IAVS) of the University of Michigan-Dearborn. The goal of the project was to show the feasibility of a cost effective family of vehicles for low volume markets and reduce the body structure mass while meeting current industry and federal standards. For low volume markets the cost of using spaceframe construction is more economical compared to the traditional unibody construction. Cost is further reduced because a family of low volume vehicles would share a lot of common parts. Also, spaceframes can use extruded sections joint by cast nodes, which are both inexpensive. The results discussed in this paper show the CAE work done on beam models for a family of vehicles including, sedan, station wagon, hatchback and SUV.

Successful simultaneous engineering requires a team with a high degree of engineering skill and experience, knowledge of the latest materials, processes, and methodologies; and it also requires finely honed people skills. Designing the workplace for people (DFP) can facilitate collaboration, increase quality and other significant metrics, and lead to an enhanced product greatly appreciated by the customer. Design for People applies the principles of performance technology to select and retain outstanding engineers, systematically train and educate for future competency needs, and reward and motivate through traditional and non-traditional approaches. Examples of best practice enable other organizations to apply the concepts of DFP.

New supplier / manufacturer relationship are necessary to produce products quickly, cost-effectively, and with features expected by the customer. However, the need for a new relationship is not universally accepted and endorsed. Resistance can be minimized through supplier self-assessment (such as Ford Motor Company's web-based instruments), management initiatives, and incentives. Trust and sharing are hallmarks. This strategy requires a new workplace paradigm affecting culture and people issues. Teams, extend across companies, share ideas and innovations. Decisions need to be mutually beneficial and the long-term value, for supplier and manufacturer, needs to be considered.

Rising costs continue to be a problem within the automotive industry. One way to address these rising costs is through modularity. Modular systems provide the ability to achieve product variety through the combination and standardization of components. Modular design approaches used in development of vehicle electrical, electronic, and software (EES) systems allow sharing of architectures/modules between different product lines (vehicles). This modular design approach may provide economies of scale, reduced development time, reduced order lead-time, easier product diagnostics, maintenance and repair. Other benefits of this design approach include development of a variety of EES systems through component swapping and component sharing. In this paper, new optimization algorithms and software tools are presented that allow vehicle EES system design engineers to develop modular architectures/modules that can be shared across vehicle platforms (for OEMs) and across OEMs (for suppliers).

A fixed-base driving simulator with a 14-degree of freedom vehicle dynamics model was used to compare the lane tracking performance of test subjects using a joystick steering controller to that using a conventional steering wheel. Three driving situations were studied: a) straight-line highway driving, b) winding road driving (country road), and c) evasive maneuvering - a double lane change event. In addition, three different joystick force-feedback settings were evaluated: i) linear force feedback, ii) non-linear, speed sensitive force feedback and iii) no force feedback. A conventional steering wheel with typical passenger car force feedback tuning was used for all of the driving events for comparison.

To reliably implement driver-assist features and ultimately self-driving cars, autonomous driving systems will likely rely on a variety of sensor types including GPS, RADAR, LASER range finders, and cameras. Cameras are an essential sensory component because they lend themselves to the task of identifying object types that a self-driving vehicle is likely to encounter such as pedestrians, cyclists, animals, other cars, or objects on the road. In this paper, we present a feature-based visual odometry algorithm based on a stereo-camera to perform localization relative to the surrounding environment for purposes of navigation and hazard avoidance. Using a stereo-camera enhances the accuracy with respect to monocular visual odometry. The algorithm relies on tracking a local map consisting of sparse 3D map points. By tracking this map across frames, the algorithm makes use of the full history of detected features which reduces the drift in the estimated motion trajectory.

This study was conducted to develop and validate a multidimensional measure of shift quality as perceived by drivers during kick-down shift events for automatic transmission vehicles. As part of the first study, a survey was conducted among common drivers to identify primary factors used to describe subjective gear-shifting qualities. A factor analysis on the survey data revealed four semantic subdimensions. These subdimensions include responsiveness, smoothness, unperceivable, and strength. Based on the four descriptive terms, a measure with semantic scales on each subdimension was developed and used in an experiment as the second study. Twelve participants drove and evaluated five vehicles with different gear shifting patterns. Participants were asked to make kick-down events with two different driving intentions (mild vs. sporty) across three different speeds on actual roadway (local streets and highway).

Six Sigma is a comprehensive and flexible methodology for achieving, maintaining, and maximizing business success by sustaining a disciplined use of facts, data, and statistics while managing, improving, and reinventing business processes based on customer requirements and cost targets. Six Sigma is a holistic approach to reducing concerns tied directly to achieving organizational objectives. AutoAlliance uses structured processes based on problem-solving methodologies known as D-M-A-I-C: define - measure - analyze - improve - control. Six Sigma deployment involves hard work, frustration, starts/stops, and disappointments that go along with launching a long-term culture change. AutoAlliance's goal is to have the entire workforce utilizing the methodologies in their everyday work. At that point, the “Six Sigma” change is complete.