No abstract

One of the most difficult aspects of any new human-machine interface development is that of evaluating the user's subjective reaction to the system. In some cases, fears about the usability of a new technology may actually prevent its approval for use due to the perception of potential risk. This is especially true in the automotive industry where driver interface design directly affects safety. In this case study, a new evaluation framework is presented: Clarity of View. It incorporates both the results of the industrypublished methods for evaluating a rearview vision system and combine with it additional new factors that contain both subjective perception and quantitative elements.This AHP based evaluation framework is more closely aligned with the actual decision making process of fleet owners in the trucking industry who are often bombarded with information about potential new safety technology for their fleets, but may otherwise have had a difficult time sorting through the many dissimilar elements of information.This case study serves as a potential model not only for other driver interface systems within the automotive industry, but for any industry that needs to consider both quantitative measures alongside subjective user perception in order to make a fully informed technology selection decisions.

Several emerging technologies hold great promise to improve the 360-degree awareness of the heavy vehicle driver. However, current industry-standard evaluation methods do not measure all the comprehensive factors contributing to the overall effectiveness of such systems. As a result, industry is challenged to evaluate new technologies in a way that is objective and allows the comparison of different systems in a consistent manner. This research aims to explore the methods currently in use, identify relevant factors not presently incorporated in standard procedures, and recommend best practices to accomplish an overall measurement system that can quantify performance beyond simply the field of view of a driver visibility system. We introduce a new metric, "Clarity of View," that incorporates several important factors for visibility systems including: gap acceptance, response time, and behavior accuracy. This paper provides an outline of the theoretical framework for our Clarity of View metric that prefaces an experimental approach to follow. The resulting work will allow recommendation of guidelines for design parameters Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.

One of the most difficult aspects of any new human-machine interface development is that of evaluating the user's subjective reaction to the system. In some cases, fears about the usability of a new technology may actually prevent its approval for use due to the perception of potential risk. This is especially true in the automotive industry where driver interface design directly affects safety. In this case study, a new evaluation framework is presented: Clarity of View. It incorporates both the results of the industrypublished methods for evaluating a rearview vision system and combine with it additional new factors that contain both subjective perception and quantitative elements.This AHP based evaluation framework is more closely aligned with the actual decision making process of fleet owners in the trucking industry who are often bombarded with information about potential new safety technology for their fleets, but may otherwise have had a difficult time sorting through the many dissimilar elements of information.This case study serves as a potential model not only for other driver interface systems within the automotive industry, but for any industry that needs to consider both quantitative measures alongside subjective user perception in order to make a fully informed technology selection decisions.