Science of Emotion and Sensibility (감성과학)

Korean Society for Emotion and Sensibility (한국감성과학회)
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Collecting the Information Needs of Skilled and Be-ginner Drivers Based on a User Mental Model for a Cus-tomized AR-HUD Interface

  • Zhang, Han (Graduate School of Comprehensive Human Sciences, University of Tsukuba) ;
  • Lee, Seung Hee (Faculty of Art and Design, University of Tsukuba)
  • Received : 2021.07.05
  • Accepted : 2021.09.03
  • Published : 2021.12.31
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Abstract

The continuous development of in-vehicle information systems in recent years has dramatically enriched drivers' driving experience while occupying their cognitive resources to varying degrees, causing driving distraction. Under this complex information system, managing the complexity and priority of information and further improvement in driving safety has become a key issue that needs to be urgently solved by the in-vehicle information system. The new interactive methods incorporating the augmented reality (AR) and head-up display (HUD) technologies into in-vehicle information systems are currently receiving widespread attention. This superimposes various onboard information into an actual driving scene, thereby meeting the needs of complex tasks and improving driving safety. Based on the qualitative research methods of surveys and telephone interviews, this study collects the information needs of the target user groups (i.e., beginners and skilled drivers) and constructs a three-mode information database to provide the basis for a customized AR-HUD interface design.

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References

  1. Abdi, L., & Meddeb, A. (2018). In-vehicle augmented reality system to provide driving safety information. Journal of Visualization, 21(1), 163-184. DOI: 10.1007/s12650-017-0442-6
  2. Amditis, A., Andreone, L., Pagle, K., Markkula, G., Deregibus, E., Romera Rue, M., Bellotti, F., Engelsberg, A., Brouwer, R., Peters, B., & De Gloria, A. (2010). Towards the automotive HMI of the future: Overview of the AIDE - integrated project results. IEEE Transactions on Intelligent Transportation Systems, 11(3), 567-578. DOI: 10.1109/TITS.2010.2048751
  3. Ardito, C., Buono, P., Caivano, D., Costabile, M. F., & Lanzilotti, R. (2014). Investigating and promoting UX practice in industry: An experimental study. International Journal of Human Computer Studies, 72(6), 542-551. DOI: 10.1016/j.ijhcs.2013.10.004
  4. Campbell, J. L., Brown, J. L., Graving, J. S., Richard, C. M., Lichty, M. G., Bacon, L. P., Morgan, J. F., & Sanquist, T. (2018). Human factors design principles for level 2 and level 3 automated driving concepts. Highway Traffic Safety Administration, National Department of Transportation, August, 122. DOI: 10.13140/RG.2.1.1874.7361
  5. Drummond, A. E. (1989). An overview of novice driver performance issues. October, 55. DOI: 10.1177/154193129303700922
  6. Firth, M. (2019). Introduction to automotive augmented reality head-up displays using TI DLP technology. DOI: 10.1364/OE.404318
  7. Han, H. (1998). A study on the effects of mental model on Interactive system design. Science of Emotion & Sensibility, 105-111.
  8. Heckman, J. J., Pinto, R., & Savelyev, P. A. (1967). Situational awareness rating technique (Sart): The development of a tool for aircrew systems design. In Angewandte Chemie International Edition, 6(11), 951-952. https://doi.org/10.1002/anie.196709511
  9. Maroto, M., Cano, E., Gonzalez, P., & Villegas, D. (2018). Head-up displays (HUD) in driving. DOI: 10.1109/ITSC.2019.8917204
  10. Nacheva, R. (2015). The importance of users' mental models for developing usable human-machine interfaces. March, 132-135. DOI: 10.18421/TEM84-54
  11. Norman, D. A. (2004). Emotional design: Why we love (or Hate) everyday things. The Journal of American Culture, 27(2), 234-234.
  12. Reason, J., Manstead, A., Stephen, S., Baxter, J., & Campbell, K. (1990). Errors and violations on the roads: A real distinction?. Ergonomics, 33(10-11), 1315-1332. DOI: 10.1080/00140139008925335
  13. Robb, E. R., & Cashen, D. (2017). Augmented reality human-machine interface: Defining future AR system technology. 2014 Vehicle Displays and Interfaces.
  14. Schneider, M., Bruder, A., Necker, M., Schluesener, T., Henze, N., & Wolff, C. (2019). A real-world driving experiment to collect expert knowledge for the design of AR HUD navigation that covers less. Mensch Und Computer 2019 - Workshop band, 410-420. DOI: 10.18420/muc2019-ws-610
  15. Strategy, A. D., Behavior, H., Young, I., & Veen, J. (n.d.). Aligning design strategy with human behavior how to use this book.
  16. Ugoni, A., & Walker, B. F. (2014). The chi square test an introduction. COMSIG Review. 1995 Nov 1; 4(3), 61-4. PMID: 17989754; PMCID: PMC2050386.
  17. Valverde, R. (2011). Principles of human computer interaction design. Lambert Academic Books, August, 1-113.
  18. Von Saucken, C., & Gomez, R. (2014). Unified user experience model enabling a more comprehensive understanding of emotional experience design. 9th International Conference on Design and Emotion 2014: The Colors of Care, 631-640.
  19. Weber, J. B. (1999). Applying visual basic for human machine interface applications.
  20. Wu, T., Dufford, A. J., Egan, L. J., Mackie, M. A., Chen, C., Yuan, C., Chen, C., Li, X., Liu, X., Hof, P. R., & Fan, J. (2018). Hick-hyman law is mediated by the cognitive control network in the brain. Cerebral Cortex, 28(7), 2267-2282. DOI: 10.1093/bhx127
  21. Yang, H., & Wu, Z. (2017). Effect of novice driver's car-following characteristic on roadway segment capacity. Advances in Mechanical Engineering, 9(7), 1-11. DOI: 10.1177/1687814017711853
  22. Yang, L., Zhang, X., Zhu, X., Luo, Y., & Luo, Y. (2019). Research on risky driving behavior of novice drivers. Sustainability (Switzerland), 11(20), 1-20. DOI: 10.3390/su11205556
  23. ZHANG, H., WANG, J., & LEE, S. (2020). Take-over request in highly automated driving: A survey on driving experience and emergency operation accuracy. International Symposium on Affective Science and Engineering, ISASE2020(0), 1-4. DOI: 10.5057/isase.2020-C000033