Methods to Validate Automotive User Interfaces Within Immersive Driving Environments

Chapter
First Online:
Part of the Human–Computer Interaction Series book series (HCIS)

Abstract

To ensure safety and usability of automotive user interfaces, prospective validations during early prototyping stages are important, especially when developing innovative human-cockpit interactions (HCI). Real car driving studies are difficult to control, manipulate, replicate, and standardize. Additionally, compared to other study designs, they are also time consuming and expensive. One economizing approach is the implementation of immersive driving environments in simulator studies to provide users a more realistic awareness of the situation. Using simulator test environments puts the question of driving simulator validity forward, meaning the extent to which results generated in simulated environments can be transferred to real world environments. Thus, in this chapter the ‘Immersive model-based HCI validation method’, which was developed by the authors, will be introduced. First, the state of the art of driving simulators will be analyzed. For this, the authors defined the degree of fidelity based on the used elements. Next, findings of a series of driving simulator tests will be presented, which investigate the influence of immersive parameters in driving environments. Visual and auditory immersive parameters were used to analyze the validity of driving simulator environments, as well as different technologies (HMD, holobench, PC). Different levels of immersion (from low to high fidelity) were tested to examine this methodology. Thus, main intention was to demonstrate the generalizability and transferability of the ‘Immersive model-based HCI validation method’ for different use cases. Objective and subjective data show advantages regarding the situational awareness and perception for highly immersive driving environments while interacting with a navigation system.

Keywords

Situation Awareness Liquid Crystal Display Stereo Vision Driving Simulator Simulator Sickness 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

References

  1. Ablassmeier, M. (2009). Multimodales, kontextadaptives Informationsmanagement im Automobil. Doctoral Thesis. Technische Universität München.Google Scholar
  2. Ballou, G. (2013). Handbook for sound engineers (4th ed., p. 597). Taylor and Francis. ISBN: 1136122532.Google Scholar
  3. Bella, F. (2008). Driving simulator for speed research on two-lane rural roads. Accident Analysis and Prevention, 40(3), 1078–1087.CrossRefGoogle Scholar
  4. Breuer, J., Bengler, K., Heinrich, C. & Reichelt, W. (2003). Development of advanced driver attention metrics (ADAM). In H. Strasser, K. Kluth, H. Rausch, & H. Bubb (Eds.). Quality of Work and Products in Enterprises of the Future. Proceedings of the 50th—Anniversary Conference of the GfA and the XVII Annual ISOES Conference in Munich, 2003 (pp. 37–40). Stuttgart: Ergonomia.Google Scholar
  5. Bruder, R., Abendroth, B. & Landau, K. (2007). Zum Nutzen von Fahrversuchen für die Gestaltung. In R. Bruder & H. Winner (Hrsg.) (Eds.), Wie objektiv sind Fahrversuche? Darmstädter Kolloquium Mensch & Fahrzeug, S. 79–93, Stuttgart: Ergonomia.Google Scholar
  6. Bubb, H., Bengler, K., Grünen, R. E., & Vollrath, M. (2015). Automobilergonomie. Wiesbaden: Springer Verlag.CrossRefGoogle Scholar
  7. Davis, J., Hsieh, Y.-H., & Lee, H.-C. (2015). Humans perceive flicker artifacts at 500 Hz. In Scientific Reports 5, Article Number 7861. doi: 10.1038/srep07861.
  8. Deutsche Ophthalmologische Gesellschaft e.V. (2013). Fahreignungsbegutachtung für den Straßenverkehr, 6. Auflage, München.Google Scholar
  9. De Winter (2007). Driving simulator fidelity and training effectiveness. In P. A. Wieringa1, J. Dankelman1, M. Mulder2, M. M. van Paassen2 & S. de Groot1 (Eds.), BioMechanical Engineering Department Faculty of Mechanical, Maritime and Materials Engineering Delft University of Technology 2628 CD Delft, The Netherlands J.C.F.deWinter@TUDelft.NL.Google Scholar
  10. Dierig, C. & Jüngling T. (2009). Der Boom der LCD-Fernseher geht zu Ende, WeltN24 GmbH, Abruf 08.01.2009.Google Scholar
  11. Elze, T., & Tanner, T. G. (2012). Temporal properties of liquid crystal displays: Implications for vision science experiments. PLoS ONE, 7(9), e44048. doi: 10.1371/journal.pone.0044048.CrossRefGoogle Scholar
  12. Endsley, M. R. (1995). Measurement of situation awareness in dynamic systems. Human Factors, 37(1), 65–84.CrossRefGoogle Scholar
  13. German RAA (2008). Forschungsgesellschaft für Straßen- und Verkehrswesen e.V., Richtlinien für die Anlage von Autobahnen (RAA), FGSV-Verlag, Köln, 2008. http://www.forschungsinformationssystem.de/servlet/is/275112/.
  14. Green, P. (2005, November 30–Dezember 2). How driving simulator data quality can be improved. In Proceedings of the Driving Simulation Conference DSC Orlando.Google Scholar
  15. Hart, S., & Staveland, L. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In P. Hancock & N. Meshkati (Eds.), Human mental workload (pp. 139–183). Amsterdam: North Holland.CrossRefGoogle Scholar
  16. Hucho, W. H. (2005). Aerodynamik des Automobils – Strömungsmechanik, Wärmetechnik, Fahrdynamik, Komfort. 5. Auflage. Wiesbaden: Vieweg.Google Scholar
  17. ISO, (2008). Road cars—Ergonomic aspects of transport information and control systems—Simulated lane change test to assess driver distraction. ISO/TC 22/SC 13 N WG8 N579, DIS 26022.Google Scholar
  18. Jahn, G., Oehme, A., Krems, J. F. & Gelau C. (2005). Peripheral detection as a workload measure in driving: Effects of traffic complexity and route guidance system use in a driving study. Transportation Research Part F-Traffic Psychology and Behaviour 8(3), 255–275.Google Scholar
  19. Kappé, B. & van Emmerik, M.L (2005). The use of driving simulators for initial driver training and testing. Soesterberg, Netherlands.Google Scholar
  20. Kennedy, R. S., Lane, N. E., Berbaum, K. S., & Lilienthal, M. G. (1993). Simulator Sickness Questionnaire (SSQ): An enhanced method for quantifying simulator sickness. International Journal of Aviation Psychology, 3(3), 203–220.CrossRefGoogle Scholar
  21. Knappe, G., Keinath, A. & Meinecke, C. (2006). Empfehlungen für die Bestimmung der Spurhaltegüte im Kontext der Fahrsimulation. In M. R. K. Baumann, S. Leuchter, & L. Urbas (Hrsg.) (Eds.), MMI-Interaktiv, Aufmerksamkeit und Situation Awareness im Fahrzeug (Vol. 11, pp. 3–13). ISSN: 1439-7854.Google Scholar
  22. Lansdown, T. C. (2001). Causes, measures and effects of driver visual workload. In P. A. Hancock & P. Desmond (Eds.), Stress, Workload, and Fatigue. Lawrence Erlbaum Associates.Google Scholar
  23. Math, R., Mahr, A., Moniri, M. M., & Müller, C. (2013). OpenDS: A New Open-source Driving Simulator for Research, GMM-Fachbericht-AmE 2013.Google Scholar
  24. Mattes, S. (2003). The lane change task as a tool for driver distraction validation. In H. Strasser, H. Rausch, & H. Bubb (Eds.), Quality of work and products in enterprises of the future. Stuttgart: Ergonomia Verlag.Google Scholar
  25. Mayhew, D. J. (1999). The usability engineering lifecycle. San Francisco: Morgan-Kaufmann.CrossRefGoogle Scholar
  26. Moreno, R., & Mayer, R. E. (2002). Learning science in virtual reality multimedia environments: Role of methods and media. Journal of Educational Psychology, 94(3), 598–610.CrossRefGoogle Scholar
  27. Nechvatal, J. (2009). Immersive Ideals / Critical Distances (pp. 48–60). LAP Lambert Academic Publishing.Google Scholar
  28. Negele, H. J. (2007). Anwendungsgerechte Konzipierung von Fahrsimulatoren für die Fahrzeugentwicklung, Dissertation, TU München.Google Scholar
  29. Nicolas, R. (2013). The different driving cycles. http://www.car-engineer.com/the-different-driving-cycles/. Abruf 01.06.2016.
  30. PC Games Hardware, Oculus Rift: Release, Preis, Spezifikationen, Systemanforderungen; pcgameshardware.de/Oculus-Rift-Hardware, Zugriff 08.07.2016.Google Scholar
  31. Pinto, M., Cavallo, V., & Ohlmann, T. (2008). The development of driving simulators: Toward a multisensory solution. Le travail humain, 71(1), 62–95.CrossRefGoogle Scholar
  32. Reich, D. (2015, September 1–2). Comparison of immersive and non-immersive driving task environments. In International Conference and Exhibition on Automobile Engineering. Valencia, Spain.Google Scholar
  33. Reich, D. & Stark, R. (2014, September 17–19). ‘To See or not to See?’ how do eye movements change within immersive driving environments. In Adjunct Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI‚14). Seattle, WA, USA. http://dx.doi.org/10.1145/2667239.2667268.
  34. Reich, D. & Stark, R. (2015, January 5–8). The influence of immersive driving environments on human-cockpit validations. In Hawai’i International Conference on System Sciences. Kauai.Google Scholar
  35. Reich, D., Wittkugel, H. J., & Stark, R. (2014). How do immersive driving environments influence user performances and experiences? In T. Ahram, W. Karwowski & T. Marek (Eds.), Proceedings of the 5th International Conference on Applied Human Factors and Ergonomics AHFE 2014.Google Scholar
  36. Remlinger, W. (2013). Analyse von Sichtverdeckungseffekte im Fahrzeug. Dissertation, Technische Universität München.Google Scholar
  37. Roza, Z. C. (2004). Simulation Fidelity Theory and Practice: A Unified Approach to Defining, Specifying and Measuring the Realism of Simulations. Doctoral thesis: Delft University of Technology, Faculty of Aerospace Engineering, Delft.Google Scholar
  38. Scheuchenpflug, R. (2001). Measuring presence in virtual environments. In M. J. Smith, G. Salvendy, & M. R. Kasdorf (Eds.), HCI International 2001 (pp. 56–58). New Orleans: HCI International.Google Scholar
  39. Schindler, J., Kelsch J., Heesen, M., Dziennus, M., Temme G. & Baumann, M. (2013). A Collaborative Approach for the Preparation of Co-operative Multi-User Driving Scenarios. 10. Berliner Werkstatt Mensch-Maschine-Systeme, 10.–12. Okt. 2013, Berlin, Germany.Google Scholar
  40. Skluzacek, S. (2012). Angular Resolution of Human Sound Localization, Thesis, Winsconsin, USA.Google Scholar
  41. Slater, M., & Wilbur, S. (1997). A framework for immersive virtual environments (five): Speculations on the role of presence in virtual environments. Presence: Teleoperators and Virtual Environments, 6(6), 603–616.CrossRefGoogle Scholar
  42. Stern, L. (1960). Personalized stereo fidelity. In Popular mechanics part II.Google Scholar
  43. Talbot-Smith, M. (2013). Audio engineer’s reference book (p. 2.52). CRC Press. ISBN: 1136119744.Google Scholar
  44. Vlakfeld, W. P. (2005). The use of simulators in basic driver training. Leidschendam, Netherlands.Google Scholar
  45. Zoeller, M. (2015). Analyse des Einflusses ausgewählter Gestaltungsparameter einer Fahrsimulation auf die Fahrerverhaltensvalidität. Dissertation. Technischen Universität Darmstadt.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Technische Universität BerlinBerlinGermany

Personalised recommendations