Effects of Quantity and Size of Buttons of In-Vehicle Touch Screen on Drivers’ Eye Glance Behavior

References

• Alexander, A. L., Stelzer, E. M., Kim, S.H., & Kaber, D. B. (2008). Bottom-up and top-down contributors to pilot perceptions of display clutter in advanced flight deck technologies. In Proceedings of the Human Factors and Ergonomics Society, 52(18), 1180–1184. Los Angeles, CA: Sage Publications.
   Google Scholar
• Bao, S., Guo, Z., Flannagan, C., Sullivan, J., Sayer, J. R., & LeBlanc, D. (2015). Distracted driving measures: A spectral power analysis. Journal of the Transportation Research Record, (2518), 68–72.
   Web of Science ®Google Scholar
• Becker, S., Hanna, P., & Wagner, V. (2014). Human Machine Interface Design in Modern Vehicles, Encyclopedia of Automotive Engineering. Online publication. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Boyle, L. N., Lee, J. D., Peng, Y., Ghazizadeh, M., Wu, Y., Miller, E., & Jenness, J. (2013). Text reading and text input assessment in support of the NHTSA visual-manual driver distraction guidelines (Report No. DOT HS 811 820). Washington, D.C.: NHTSA.
   Google Scholar
• Brumby, D. P., Salvucci, D. D., & Howes, A. (2009). Focus on driving: How cognitive constraints shape the adaptation of strategy when dialing while driving. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1629–1638). Boston, MA: ACM.
   Google Scholar
• Burnett, G. E., & Porter, J. M. (2001). Ubiquitous computing within cars: Designing controls for non-visual use. International Journal of Human-Computer Studies, 55(4), 521–531.
   Web of Science ®Google Scholar
• Burns, P., Harbluk, J., Foley, J. P., & Angell, L. (2010). The importance of task duration and related measures in assessing the distraction potential of in-vehicle tasks. In Proceedings of the 2nd International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 12–19). Pittsburgh, PA: ACM.
   Google Scholar
• Chen, Y., Tonshal, B., Rankin, J., & Feng, F. (2016). Development of an integrated simulation system for design of speech-centric multimodal human-machine interfaces in an Automotive Cockpit Environment. In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Paper No. DETC2016-59309. Charlotte, NC.
   Google Scholar
• Chevrolet MyLink. (n.d.). Welcome to connectivity. Retrieved from http://www.chevrolet.com/owners/mylink-vehicle-technology
   Google Scholar
• Crundall, E., Large, D. R., & Burnett, G. (2016). A driving simulator study to explore the effects of text size on the visual demand of in-vehicle displays. Displays, 43, 23–29.
   Web of Science ®Google Scholar
• Dandekar, K., Raju, B. I., & Srinivasan, M. A. (2003). 3-D finite-element models of human and monkey fingertips to investigate the mechanics of tactile sense. Journal of Biomechanical Engineering, 125(5), 682–691.
   PubMed Web of Science ®Google Scholar
• Dingus, T. A., Hulse, M. C., Antin, J. F., & Wierwille, W. W. (1989). Attentional demand requirements of an automobile moving-map navigation system. Transportation Research Part A: General, 23(4), 301–315.
   Google Scholar
• Driver Focus-Telematics Working Group. (2006). Statement of principles, criteria and verification procedures on driver interactions with advanced in-vehicle information and communication systems. Washington, DC: Alliance of Automobile Manufactures.
   Google Scholar
• Feng, F., Liu, Y., & Chen, Y. (2017). A Computer-Aided Usability Testing Tool for In-Vehicle Infotainment Systems. Computers & Industrial Engineering, 109, 313–324.
   Web of Science ®Google Scholar
• Fitts, P. M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47(6), 381.
   PubMedGoogle Scholar
• Gauthier, G. M., Roll, J. P., Martin, B., & Harlay, F. (1981). Effects of whole-body vibrations on sensory motor system performance in man. Aviation, Space, and Environmental Medicine, 52(8), 473–479.
   PubMedGoogle Scholar
• Ghez, C., Gordon, J., Ghilardi, M. F., Christakos, C. N., & Cooper, S. E. (1990). Roles of proprioceptive input in the programming of arm trajectories. In Cold spring harbor symposia on quantitative biology (Vol. 55, pp. 837–847). New York, NY: Cold Spring Harbor Laboratory Press.
   Google Scholar
• Green, P. (1999). Estimating compliance with the 15-second rule for driver interface usability and safety. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 43(1), 987–991.
   Google Scholar
• Greenberg, J., Tijerina, L., Curry, R., Artz, B., Cathey, L., Kochhar, D., Kozak, K., Blommer, M., & Grant, P. (2003). Evaluation of driver distraction using an event detection paradigm. In Transportation Research Record: Journal of the Transportation Research Board, (1843), 1–9. Washington, DC: National Research Council.
   Web of Science ®Google Scholar
• Greenstein, J. S. (1997). Pointing Devices. In M. Helander & T. L. P. Prabhu (ed), Handbook of Human-Computer Interaction (pp. 1317–1348). North-Holland, Amsterdam: Elsevier Science Publishers.
   Google Scholar
• Harbluk, J. L., Noy, Y. I., & Eizenman, M. (2002). The impact of cognitive distraction on driver visual behaviour and vehicle control (No. TP# 13889 E) Ottawa, Ontario: Transport Canada.
   Google Scholar
• Harvey, C., Stanton, N. A., Pickering, C. A., McDonald, M., & Zheng, P. (2011). In-vehicle information systems to meet the needs of drivers. International Journal of Human-Computer Interaction, 27(6), 505–522.
   Web of Science ®Google Scholar
• Horrey, W., & Wickens, C. D. (2004). Driving and side task performance: The effects of display clutter, separation, and modality. Human Factors, 46(4), 611–624.
   PubMed Web of Science ®Google Scholar
• Horrey, W., & Wickens, C. D. (2007). In-vehicle glance duration: Distributions, tails, and model of crash risk. Transportation research record. Journal of the Transportation Research Board, 2018, 22–28.
   Web of Science ®Google Scholar
• International Organization for Standardization. (2010). Road vehicles - Symbols for controls, indicators and tell-tales (ISO 2575). Geneva: International Standards Organization.
   Google Scholar
• International Organization for Standardization. (2014). Road vehicles - Measurement of driver visual behaviour with respect to transport information and control systems - Part 1: Definitions and parameters (ISO 15007-1). Geneva: International Standards Organization.
   Google Scholar
• Iqbal, S. T., & Bailey, B. P. (2005, April). Investigating the effectiveness of mental workload as a predictor of opportune moments for interruption. In CHI’05 Extended Abstracts on Human Factors on Computing Systems (pp. 1489–1492). Portland, OR: ACM.
   Google Scholar
• Iqbal, S. T., & Bailey, B. P. (2006). Leveraging characteristics of task structure to predict the cost of interruption. In Proceedings of the SIGCHI conference on Human Factors in computing systems, 741–750. Montréal, Québec, Canada.
   Google Scholar
• Janssen, C. P., Brumby, D. P., & Garnett, R. (2012). Natural break points: The influence of priorities and cognitive and motor cues on dual-task interleaving. Journal of Cognitive Engineering and Decision Making, 6(1), 5–29.
   Google Scholar
• Jeong, H., & Liu, Y. (2017). Effects of touchscreen gesture’s type and direction on finger-touch input performance and subjective ratings. Ergonomics, 60(11), 1528–1539.
   PubMed Web of Science ®Google Scholar
• Jin, Z. X., Plocher, T., & Kiff, L. (2007). Touch screen user interfaces for older adults: Button size and spacing. In Proceedings of the 4th International Conference on Universal Access in Human Computer Interaction: Coping with Diversity, Beijing, China, (pp. 933–941).
   Google Scholar
• Klauer, S. G., Dingus, T. A., Neale, V. L., Sudweeks, J. D., & Ramsey, D. J. (2006). The impact of driver inattention on near-crash/crash risk: An analysis using the 100-car naturalistic driving study data (Report No. DOT HS 810 594). Washington, DC: National Highway Traffic Safety Administration.
   Google Scholar
• Kujala, T. (2013). Browsing the information highway while driving: Three in-vehicle touch screen scrolling methods and driver distraction. Personal and Ubiquitous Computing, 17(5), 815–823.
   Web of Science ®Google Scholar
• Kujala, T., & Saariluoma, P. (2011). Effects of menu structure and touch screen scrolling style on the variability of glance durations during in-vehicle visual search tasks. Ergonomics, 54(8), 716–732.
   PubMed Web of Science ®Google Scholar
• Kujala, T., & Salvucci, D. D. (2015). Modeling visual sampling on in-car displays: The challenge of predicting safety-critical lapses of control. International Journal of Human-Computer Studies, 79, 66–78.
   Web of Science ®Google Scholar
• Lansdown, T. C., Brook-Carter, N., & Kersloot, T. (2004). Distraction from multiple in-vehicle secondary tasks: Vehicle performance and mental workload implications. Ergonomics, 47(1), 91–104.
   PubMed Web of Science ®Google Scholar
• Large, D. R., Burnett, G., Crundall, E., Van Loon, E., Eren, A. L., & Skrypchuk, L. (2017). Developing predictive equations to model the visual demand of in-vehicle touchscreen HMIs. International Journal of Human-Computer Interaction. doi: 10.1080/10447318.2017.1306940
   Web of Science ®Google Scholar
• Lee, J. D. (2007). Technology and teen drivers. Journal of Safety Research, 38(2), 203–213.
   PubMed Web of Science ®Google Scholar
• Lee, J. Y., Gibson, M., & Lee, J. D. (2015). Secondary task boundaries influence drivers’ glance durations. In Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 273–280). Nottingham, UK: ACM.
   Google Scholar
• Lee, J. Y., & Lee, J. D. (2017). Modeling the effect of subtask boundaries on driver glance behavior. In Proceedings of the Ninth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, (pp.389–395), Manchester Village, VT..
   Google Scholar
• Lee, S. C., Hwangbo, H., & Ji, Y. G. (2016). Perceived Visual Complexity of In-Vehicle Information Display and Its Effects on Glance Behavior and Preferences. International Journal of Human-Computer Interaction, 32(8), 654–664.
   Web of Science ®Google Scholar
• Miyata, Y., & Norman, D. A. (1986). Psychological issues in support of multiple activities. In D. A. Norman & S. W. Draper (Eds.), User centered system design: New perspectives on human-computer interaction, (pp. s265–284)Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
   Google Scholar
• Moacdieh, N., & Sarter, N. (2015). Display clutter a review of definitions and measurement techniques. Human Factors, 57(1), 61–100.
   PubMed Web of Science ®Google Scholar
• Monterey Technologies, Inc. (1996). Resource Guide for Accessibility: Design of Consumer Electronics. Washington, DC: Draft Submitted to: EIA-EIF Committee on Product Accessibility A Joint Venture of the Electronic Industries Association and the Electronic Industries Foundation. 20006.
   Google Scholar
• National Center for Statistics and Analysis. (2017). Distracted driving 2015. Traffic safety facts research note (Report No. DOT HS 812 381). Washington, DC: National Highway Traffic Safety Administration.
   Google Scholar
• NHTSA. (2012). Visual-manual NHTSA driver distraction guidelines for in-vehicle electronic devices. Washington, DC: National Highway Traffic Safety Administration.
   Google Scholar
• Normark, C. J. (2015). Design and evaluation of a touch-based personalizable in-vehicle user interface. International Journal of Human-Computer Interaction, 31(11), 731–745.
   Web of Science ®Google Scholar
• Nothdurft, H. (2000). Salience from feature contrast: Variations with texture density. Vision Research, 40(23), 3181–3200.
   PubMed Web of Science ®Google Scholar
• Noy, Y. I., Lemoine, T. L., Klachan, C., & Burns, P. C. (2004). Task interruptability and duration as measures of visual distraction. Applied Ergonomics, 35(3), 207–213.
   PubMed Web of Science ®Google Scholar
• Pavlidis, I., Dcosta, M., Taamneh, S., Manser, M., Ferris, T., Wunderlich, R., … Tsiamyrtzis, P. (2016). Dissecting driver behaviors under cognitive, emotional, sensorimotor, and mixed stressors. Scientific Reports, 6, 1–12.
   PubMed Web of Science ®Google Scholar
• Payne, S. J., Duggan, G. B., & Neth, H. (2007). Discretionary task interleaving: Heuristics for time allocation in cognitive foraging. Journal of Experimental Psychology General, 136(3), 370.
   PubMed Web of Science ®Google Scholar
• Peng, Y., Boyle, L. N., & Hallmark, S. L. (2013). Driver’s lane keeping ability with eyes off road: Insights from a naturalistic study. Accident Analysis & Prevention, 50, 628–634.
   PubMed Web of Science ®Google Scholar
• Rubin, J., & Chisnell, D. (2008). Handbook of usability testing: Howto plan, design, and conduct effective tests. Indianapolis, IN: Wiley Publishing, Inc..
   Google Scholar
• Salvucci, D. D., & Kujala, T. (2016). Balancing Structural and Temporal Constraints in Multitasking Contexts. In A. Papafragou, D. Grodner, D. Mirman, & J. Trueswell (Eds.), Austin, TX: CogSci 2016: Proceedings of the 38th Annual Conference of the Cognitive Science Society, (pp. 2465–2470). ISBN 978-0-9911967-3-9. Cognitive Science Society.
   Google Scholar
• Sauro, J., & Lewis, J. R. (2010). Average task times in usability tests: What to report? In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2347–2350), Atlanta, GA: ACM.
   Google Scholar
• Senders, J. W., Kristofferson, A. B., Levison, W. H., Dietrich, C. W., & Ward, J. L. (1967). The attentional demand of automobile driving. Highway research record, 195(1967), 15–33.
   Google Scholar
• Society of Automotive Engineers. (1993). Manual controls for mature drivers (SAE J2119) Warrendale, PA: SAE International.
   Google Scholar
• Society of Automotive Engineers. (2000). Definitions and experimental measures related to the specification of driver visual behavior using video based techniques (SAE J2396). Warrendale, PA: SAE International.
   Google Scholar
• Society of Automotive Engineers. (2004). Navigation and route guidance function accessibility while driving (SAE J2364). Warrendale, PA: SAE International.
   Google Scholar
• Stevens, A., Quimby, A., Board, A., Kersloot, T., & Burns, P. (2002). Design guidelines for safety of in-vehicle information systems. Wokingham, UK: Transport Research Laboratory.
   Google Scholar
• Tsimhoni, O., & Green, P. (2001). Visual demand of driving and time execution of display intensive in-vehicle tasks. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 45, 1586–1590.
   Google Scholar
• Tsimhoni, O., Smith, D., & Green, P. (2004). Address entry while driving: Speech recognition versus a touch-screen keyboard. Human Factors, 46(4), 600–610.
   PubMed Web of Science ®Google Scholar
• Vlaskamp, B. N., & Hooge, I. (2006). Crowding degrades saccadic search performance. Vision Research, 46(3), 417–425.
   PubMed Web of Science ®Google Scholar
• Wickens, C. D., & Carswell, C. M. (1995). The proximity compatibility principle: Its psychological foundation and relevance to display design. Human Factors, 37, 473–494.
   Web of Science ®Google Scholar
• Wierwille, W. W. (1993a). Demands on driver resources associated with introducing advanced technology into the vehicle. Transportation Research Part C: Emerging Technologies, 1(2), 133–142.
   Google Scholar
• Wierwille, W. W. (1993b). Visual and manual demands of in-car controls and displays. In B. Peacock & W. Karwowsk (Eds.), Automotive Ergonomics (pp. 299–320). London, UK: Taylor and Francis.
   Google Scholar
• Wierwille, W. W., & Tijerina, L. (1998). Modelling the relationship between driver in-vehicle visual demands and accident occurrence. In A. G. Gale, I. D. Brown, C. M., Haslegrave and S. P. Taylor (eds), Vision in Vehicles - VI, Amsterdam: Elsevier, (pp. 233–244).
   Google Scholar
• Wobbrock, J. O., Findlater, L., Gergle, D., & Higgins, J. J. (2011). The aligned rank transform for nonparametric factorial analyses using only anova procedures. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 143–146), Vancouver, BC: ACM.
   Google Scholar
• Wolfe, J. M. (2007). Guided Search 4.0: Current progress with a model of visual search. In W. Gray (Ed.), Integrated Models of Cognitive Systems (pp. 99–119). Oxford: New York, NY: Oxford University Press.
   Google Scholar
• Yoon, S. H., Lim, J. H., & Ji, Y. G. (2015). Perceived visual complexity and visual search performance of automotive instrument cluster: A quantitative measurement study. International Journal of Human-Computer Interaction, 31(12), 890–900.
   Web of Science ®Google Scholar