Assisting the Driver with Distance Estimation: Usability Evaluation of Graphical Presentation Alternatives for Local Traffic Events

  • Angela Mahr
  • Sandro Castronovo
  • Rafael Math
  • Christian Müller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8119)


When integrating numerous in-car information and assistance systems, a consistent way of spatial distance presentation for drivers is required. A common practice is to use discrete textual information (e.g. 500 meters) in combination with a graphical bar representing relative spatial information. Hitherto there exists no design consistency with respect to bars for distance illustration. Contemporary solutions differ in terms of movement direction (upward vs. downward), composition type (decreasing vs. increasing), or alignment (horizontal vs. vertical). We conducted a driving simulator experiment to investigate user preferences, perceived location, and eye gaze data for a meaningful subset of bars in a dynamic scenario. When approaching a traffic event (road works), one out of four vertical bar alternatives indicated the current distance. Subsequently, the associated horizontal bar type (decreasing or increasing) visualized the driver’s spatial progress within the road works section. Our results indicate, that drivers prefer upwards-moving approach bars and progress bars decreasing from left to right. Eye-tracking data supports usage of decreasing bars instead of increasing bars. Accordingly, we elaborated an initial version of design guidelines for bars representing relative spatial information for local events. On this basis we implemented approach and progress indicators, which were adopted for numerous use-cases in a large field operational test for Vehicle-2-X Communication.


Distance Assistance Design Usability Evaluation In-car Driving 


  1. 1.
    “Driver Focus-Telematics Working Group”: Statement of Principles, Criteria and Verification Procedures on Driver Interactions with Advanced In-Vehicle Information and Communication Systems (2006),
  2. 2.
    Hamada, K., Yoshida, K., Ohnishi, K., Koppen, M.: Color Effect on Subjective Perception of Progress Bar Speed. In: Third IEEE International Conference on Intelligent Networking and Collaborative Systems, pp. 863–866. Institute of Electrical and Electronics Engineers (IEEE), Fukuoka (2011)CrossRefGoogle Scholar
  3. 3.
    Harrison, C., Yeo, Z., Hudson, S.E.: Faster Progress Bars: Manipulating Perceived Duration with Visual Augmentations. In: Proceedings of the 28th Annual SIGCHI Conference on Human Factors in Computing Systems, pp. 1545–1548. ACM, New York (2010)Google Scholar
  4. 4.
    Harrison, C., Amento, B., Kuznetsov, S.: Rethinking the progress bar. In: Proceedings of the 20th Annual ACM Symposium on User interface Software and Technology, pp. 115–118. ACM, New York (2007)CrossRefGoogle Scholar
  5. 5.
    Hsu, S., Le Prado, C., Natkin, S., Liard, C.: New Type of Auditory Progress Bar: Exploration, Design and Evaluation. In: Stephanidis, C. (ed.) UAHCI 2007 (Part II). LNCS, vol. 4555, pp. 868–877. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Brewster, S.A., King, A.: The design and evaluation of a vibrotactile progress bar. In: First Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, pp. 499–500. Institute of Electrical and Electronics Engineers (IEEE), Pisa (2005)CrossRefGoogle Scholar
  7. 7.
    Myers, B.A.: The importance of percent-done progress indicators for computer-human interfaces. In: Borman, L., Curtis, B. (eds.) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 11–17. ACM, New York (1985)Google Scholar
  8. 8.
    Math, R., Mahr, A., Moniri, M.M., Müller, C.: OpenDS: A new open-source driving simulator for research. In: Adjunct Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Appilcations, Portsmouth, NH, USA, pp. 7–8.,
  9. 9.
    Paßmann, C., Schaaf, G., Naab, K.: simTD Project result: Selection of Functions, Deliverable D11.2 (2009),
  10. 10.
    “the International Organization for Standardization”: ISO/TR 16352: Road vehicles - Ergonomic aspects of in-vehicle presentation for transport information and control systems - Warning systems (2005)Google Scholar
  11. 11.
    Dien, J., Santuzzi, A.M.: Application of repeated-measures ANOVA to high-density ERP datasets: A review and tutorial. In: Handy, T. (ed.) Event-Related Potentials: a Methods Handbook, pp. 57–82. MIT Press, Cambridge (2005)Google Scholar
  12. 12.
    O’Brien, R., Kaiser, M.: MANOVA method for analyzing repeated measures designs: An extensive primer. Psychological Bulletin 97, 316–333 (1985)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Angela Mahr
    • 1
  • Sandro Castronovo
    • 1
  • Rafael Math
    • 1
  • Christian Müller
    • 1
  1. 1.Intelligent User Interfaces DepartmentGerman Research Center for Artificial IntelligenceSaarbrückenGermany

Personalised recommendations