Modeling Driver Distraction from Cognitive Tasks

Driver distraction has become a critical area of study both for research in investigating human multitasking abilities and for practical purposes in developing and constraining new in-vehicle devices. This work utilizes an integrated-model approach to predict driver distraction from a primarily cognitive secondary task. It integrates existing models for a sentence-span task and driving task and investigates two methods in which the resulting model can perform multitasking. Model predictions correspond well qualitatively to two of three measures of driver performance as collected in a recent empirical study. The paper includes a discussion of the potential for building multitasking models in the framework of a production-system cognitive architecture.