ABSTRACT
As driver distraction from in-vehicle devices increasingly becomes a concern on our roadways, researchers have searched for better scientific understanding of distraction along with better engineering tools to build less distracting devices. This paper presents a new system, Distract-R, that allows designers to rapidly prototype and evaluate new in-vehicle interfaces. The core engine of the system relies on a rigorous cognitive model of driver performance, which the system integrates 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. The paper includes two sample studies that demonstrate the system's ability to account for effects of input modality and driver age on performance.
- Anderson, J. R., & Lebiere, C. (1998). The atomic components of thought. Hillsdale, NJ: Erlbaum.Google Scholar
- Card, S. K., Moran, T.P. and Newell, A. (1983). The psychology of human-computer interaction. Hillsdale, NJ: Lawrence Erlbaum Associates. Google ScholarDigital Library
- Green, P. (1999). The 15-second rule for driver information systems. In ITS America Ninth Annual Meeting Conference Proceedings. Washington, D.C.: Intelligent Transportation Society of America.Google Scholar
- Hendricks, D. L., Freedman, M., Zador, P. L., & Fell, J. C. (2001). The relative frequency of unsafe driving acts in serious traffic crashes. Report for Contract No. DTNH22-94-C-05020, National Highway Traffic Safety Administration, U.S. Department of Transportation.Google Scholar
- Hudson, S. E., John, B. E., Knudsen, K., and Byrne, M. D. (1999). A tool for creating predictive performance models from user interface demonstrations. CHI Letters 1 (1), 93--102.Google Scholar
- IVIS DEMAnD Prototype Software User's Manual, pub. No. 00-136, USDOT, Aug. 2000, http://www.tfhrc.gov/humanfac/00-136.pdf.Google Scholar
- John, B. E., Prevas, K., Salvucci, D. D., & Koedinger, K. (2004). Predictive human performance modeling made easy. In Human Factors in Computing Systems: CHI 2004 Conference Proceedings (pp. 455--462). New York: ACM Press. Google ScholarDigital Library
- John, B. E., Salvucci, D. D., Centgraf, P., & Prevas, K. (2004). Integrating models and tools in the context of driving and in-vehicle devices. In Proceedings of the Sixth International Conference on Cognitive Modeling.Google Scholar
- Landay, J. A., & Myers, B. A. (1995). Interactive sketching for the early stages of user interface design. In Proceedings of Human Factors in Computing Systems: CHI 95 (pp. 43--50). New York: ACM Press. Google ScholarDigital Library
- Paganelli, L., & Paternòò, F. (2003). A tool for creating design models from web site code. International Journal of Software Engineering and Knowledge Engineering, 13, 169--189.Google ScholarCross Ref
- Reed, M. P & Green, P. A. (1999). Comparison of driving performance on-road and in a low-cost driving simulator using a concurrent telephone dialing task. Ergonomics, 42, 1015--1037.Google ScholarCross Ref
- Salvucci, D. D. (2001). Predicting the effects of in-car interface use on driver performance: An integrated model approach. International Journal of Human-Computer Studies, 55, 85--107.Google ScholarDigital Library
- Salvucci, D. D. (2002). Modeling driver distraction from cognitive tasks. In Proceedings of the 24th Annual Conference of the Cognitive Science Society (pp. 792--797). Mahwah, NJ: Erlbaum.Google Scholar
- Salvucci, D. D. (to appear). Modeling driver behavior in a cognitive architecture. Human Factors.Google Scholar
- Salvucci, D. D., Chavez, A. K., & Lee, F. J. (2004). Modeling effects of age in complex tasks: A case study in driving. To appear in Proceedings of the 26th Annual Conference of the Cognitive Science Society.Google Scholar
- Salvucci, D. D., John, B. E., Prevas, K., & Centgraf, P. (2005). Rapid development of keystroke-level models for on- and off-the-desktop interfaces. Manuscript in preparation.Google Scholar
- Salvucci, D. D., & Lee, F. J. (2003). Simple cognitive modeling in a complex cognitive architecture. In CHI 2003 Conference Proceedings (pp. 265--272). New York: ACM Press. Google ScholarDigital Library
- St. Amant, R., & Ritter, F. E. (2005). Specifying ACT-R models of user interaction with a GOMS language. Cognitive Systems Research. 6, 71--88. Google ScholarDigital Library
- Tijerina, L., Johnston, S., Parmer, E., Winterbottom, M.D., and Goodman, M. (2000). Driver Distraction with Route Guidance Systems (Technical Report DOT HS 809 069), East Liberty, OH: National Highway Traffic Safety Administration.Google Scholar
Index Terms
- Distract-R: rapid prototyping and evaluation of in-vehicle interfaces
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