As driver distraction from in-vehicle devices increasingly becomes a concern, researchers have searched for better scientific understanding of distraction along with better engineering tools to build less distracting devices. We have developed a new system, Distract-R, that allows designers to rapidly prototype and evaluate new in-vehicle interfaces. The core engine of the system uses a computational driver model specified in ACT-R extended with threaded cognition, along with an integrated-model approach with models of behavior on the prototyped interfaces to generate predictions of distraction. Distract-R allows a designer to prototype basic interfaces, demonstrate possible tasks on these interfaces, specify relevant driver characteristics and driving scenarios, and finally simulate, visualize, and analyze the resulting behavior as generated by the cognitive model. We have performed sample studies that demonstrate the system's ability to account for effects of input modality and driver age on performance.
This Youtube video shows a brief demonstration of the system.