Evaluating the distraction potential of connected vehicles

Authors:
Joonbum Lee;John D. Lee;Dario D. Salvucci
Affiliations:
University of Wisconsin-Madison, Madison, WI;University of Wisconsin-Madison, Madison, WI;Drexel University, Philadelphia, PA
Venue:
Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Year:
2012

Citing 3
Cited 0

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Abstract

Connected vehicles offer great potential for new sources of information, but may also introduce new sources of distraction. This paper compares three methods to quantify distraction, and focuses on one method: computational models of driver behavior. An integration of a saliency map and the Distract-R prototyping and evaluation system is proposed as a potential model. The saliency map captures the bottom-up influences of visual attention and this influence is integrated with top-down influences captured by Distract-R. The combined model will assess the effect of coordinating salient visual features and drivers' expectations, and in using both together, generate more robust predictions of performance.