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 accuracy, image detection time, and distortion. By employing the Analytic Hierarchy Process (AHP), additional subjective measures may be incorporated into this multi-factor decision-making model that is intended to help industry make technology selections that involve complex variables. 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 for acceptable performance of visibility systems specifically designed for heavy vehicles.