Files and links (1)
Published, Version of Record (VoR)Open Access (License Unspecified), Open
Conference proceeding
Evaluating the distraction potential of connected vehicles
Proceedings of the 4th International Conference on automotive user interfaces and interactive vehicular applications, pp 33-40
17 Oct 2012
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.
Metrics
25 Record Views
7 citations in Scopus
Details
- Title
- Evaluating the distraction potential of connected vehicles
- Creators
- Joonbum Lee - University of Wisconsin–MadisonJohn Lee - University of Wisconsin–MadisonDario Salvucci - Drexel UniversityJinwook Lee - Decision Sciences (and Management Information Systems)
- Publication Details
- Proceedings of the 4th International Conference on automotive user interfaces and interactive vehicular applications, pp 33-40
- Series
- AutomotiveUI '12
- Publisher
- Association for Computing Machinery (ACM)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems); Computer Science
- Scopus ID
- 2-s2.0-84870534338
- Other Identifier
- 991019173444304721