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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.

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.

The method of communication between the customer and the engineer has been refined to further improve the headlight beam pattern development process. The refinements included: a) reduction of word pairs used for semantic differential scaling and b) use of shortened questionnaire on night-roadway viewing zones. The added benefit of the new questionnaire method allows the engineer to evaluate the customer responses of the beam pattern within specific areas on the road scene. A statistical technique called factor analysis has been used to evaluate and to reduce the large number of semantic differential word pairs used in the previous work by Jack, O'Day and Bhise (1). A comparison of the two questionnaire forms used in the evaluation surveys was completed based on an evaluation of beam patterns in a dynamic drive situation.

The headlamp beam pattern development process contains both subjective and objective evaluations. The subjective evaluation, communication between the customer and the engineer, was developed in previous work [1][2]. This paper presents exploratory models used in the identification of objective photometric variables of a beam pattern that relate to the subjective impression of the beam pattern. Additional research will allow use of the photometric variables and their selected ranges for designing and evaluating beam patterns to achieve improved customer pleasing beam pattern driving experiences.

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.

Seat comfort is a highly subjective attribute and depends on a wide range of factors, but the successful prediction of seat comfort from a group of relevant variables can hold the promise of eliminating the need for time-consuming subjective evaluations during the early stages of seat cushion selection and development. This research presents the subjective seat comfort data of a group of 30 participants using a controlled range of seat foam samples, and attempts to correlate this attribute with a) the anthropometric and demographic characteristics of the participants, b) the objective pressure distribution at the body-seat interface and c) properties of the various foam samples that were used for the test.

The automotive interior suppliers are challenged to develop materials, that not only perform functionally, but also provide the right combination sensory experience (e.g. visual appeal, tactile feeling) and brand differentiation at very competitive costs. Therefore, the objective of this research presented in this paper is to develop a methodology that can be used to measure customer perception of interior materials and to come up with a unique system for assessing value of different interior materials. The overall methodology involves the application of a number of psychophysical measurement methods (e.g. Semantic Differential Scaling) and statistical methods to assess: 1) overall customer perceived quality of materials, 2) elements (or attributes) of perception, and 3) value of materials from OEM's viewpoint in terms of the measurement of perception of quality divided by a measure of cost.