Attention-Based Model of Driver Performance in Rear-End Collisions

Abstract

Several driver-performance factors contribute to rear-end collisions—driver inattention, perception-reaction time, and limitations of the human visual system. Although many evaluations have examined driver response to various rear-end collision avoidance systems (RECAS) display and algorithm alternatives, little research has been directed at creating a quantitative model of driver performance to evaluate these alternatives. Current considerations of driver behavior in developing warning algorithms tend to ignore the fundamental problem of driver inattention and assume a fixed driver reaction time with no further adjustment after the initial response. A more refined model of driver response to rear-end crash scenarios can identify more appropriate and timely information to be displayed to the driver. An attention-based rear-end collision avoidance model (ARCAM) is introduced that describes the driver’s attention distribution, information extraction and judgment process, and the reaction process. ARCAM predicts the closed-loop nature of collision response performance and explains how the driver might use RECAS warnings.