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Mental Model of Driving Wizards When Simulating an Automated Drive

Published:26 October 2020Publication History

ABSTRACT

When prototyping automated vehicles using the WoZ methodology, the driving behavior simulated by driving wizards significantly shapes participants’ experience during the experiment. Therefore, driving wizards should be provided with instructions regarding the automated driving behavior. However, they are currently instructed in different ways. It is hypothesized that different instructions lead to differing mental models and consequently to a differing realization of the automated driving behavior to be simulated. A study using a right-hand drive vehicle was conducted where participants acted as driving wizards. Three different instructions were tested at two test times (3x2 within-subjects design, N = 14). As a result, a set of rules, that driving wizards may use when simulating an automated driving style, was developed. Using qualitative & quantitative guidelines as instructions might lead to a more homogeneous mental model between several driving wizards as well as for the same driving wizard at different points in time.

References

  1. Christer Ahlstrom and Katja Kircher. 2017. Changes in glance behaviour when using a visual eco-driving system - A field study. Applied Ergonomics 58(2017), 414–423. https://doi.org/10.1016/j.apergo.2016.08.001Google ScholarGoogle ScholarCross RefCross Ref
  2. Craig K. Allison and Neville A. Stanton. 2019. Eco-driving: the role of feedback in reducing emissions from everyday driving behaviours. Theoretical Issues in Ergonomics Science 20, 2 (2019), 85–104. https://doi.org/10.1080/1463922X.2018.1484967Google ScholarGoogle ScholarCross RefCross Ref
  3. Cindie Andrieu and Guillaume Saint Pierre. 2012. Comparing Effects of Eco-driving Training and Simple Advices on Driving Behavior. Procedia - Social and Behavioral Sciences 54 (Oct. 2012), 211–220. https://doi.org/10.1016/j.sbspro.2012.09.740Google ScholarGoogle Scholar
  4. Norbert Bach. 2000. Mentale Modelle als Basis von Implementierungsstrategien: Konzepte für ein erfolgreiches Change Management. Springer Fachmedien Wiesbaden, Wiesbaden. https://doi.org/10.1007/978-3-663-08757-1Google ScholarGoogle ScholarCross RefCross Ref
  5. Sonia Baltodano, Srinath Sibi, Nikolas Martelaro, Nikhil Gowda, and Wendy Ju. 2015. The RRADS Platform: A Real Road Autonomous Driving Simulator. In 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Ayse Eren and Vicki Antrobus (Eds.). ACM Press, New York, NY, 281–288. https://doi.org/10.1145/2799250.2799288Google ScholarGoogle Scholar
  6. Hanna Bellem, Thorben Schönenberg, Josef F. Krems, and Michael Schrauf. 2016. Objective metrics of comfort: Developing a driving style for highly automated vehicles. Transportation Research Part F: Traffic Psychology and Behaviour 41 (Aug. 2016), 45–54. https://doi.org/10.1016/j.trf.2016.05.005Google ScholarGoogle Scholar
  7. Sabrina Beloufa, Fabrice Cauchard, Joël Vedrenne, Benjamin Vailleau, Andras Kemeny, Frédéric Mérienne, and Jean-Michel Boucheix. 2019. Learning eco-driving behaviour in a driving simulator: Contribution of instructional videos and interactive guidance system. Transportation Research Part F: Traffic Psychology and Behaviour 61 (Feb. 2019), 201–216. https://doi.org/10.1016/j.trf.2017.11.010Google ScholarGoogle Scholar
  8. Klaus Bengler, Kamil Omozik, and Andrea Isabell Müller. 2020. The Renaissance of Wizard of Oz (WoOz): Using the WoOz methodology to prototype automated vehicles. In Proceedings of the Human Factors and Ergonomics Society Europe Chapter 2019 Annual Conference, Dick de Waard (Ed.). 63–72.Google ScholarGoogle Scholar
  9. Klaus Bengler, Michael Rettenmaier, Nicole Fritz, and Alexander Feierle. 2020. From HMI to HMIs: Towards an HMI Framework for Automated Driving. Information 11 (Jan. 2020). https://doi.org/10.3390/info11020061Google ScholarGoogle Scholar
  10. Niels Ole Bernsen, Hans Dybkjær, and Laila Dybkjær. 1994. Wizard of Oz Prototyping: When and How?. In CCI Working Papers in Cognitive Science and HCI.Google ScholarGoogle Scholar
  11. Erik Coelingh, Jonas Nilsson, and Jude Buffum. 2018. Driving tests for self-driving cars. IEEE Spectrum 55, 3 (March 2018), 40–45. https://doi.org/10.1109/MSPEC.2018.8302386Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Stephanie Cramer, Tabea Blenk, Martin Albert, and David Sauer. 2020. Evaluation of different driving styles during conditionally automated highway driving. In Proceedings of the Human Factors and Ergonomics Society Europe Chapter 2019 Annual Conference, Dick de Waard (Ed.). 85–96.Google ScholarGoogle Scholar
  13. Fredrick Ekman, Mikael Johansson, Lars-Ola Bligård, MariAnne Karlsson, and Helena Strömberg. 2019. Exploring automated vehicle driving styles as a source of trust information. Transportation Research Part F: Traffic Psychology and Behaviour 65 (Aug. 2019), 268–279. https://doi.org/10.1016/j.trf.2019.07.026Google ScholarGoogle Scholar
  14. Carina Fors, Katja Kircher, and Christer Ahlström. 2015. Interface design of eco-driving support systems – Truck drivers’ preferences and behavioural compliance. Transportation Research Part C: Emerging Technologies 58 (Sept. 2015), 706–720. https://doi.org/10.1016/j.trc.2015.03.035Google ScholarGoogle Scholar
  15. Norman M. Fraser and G. Nigel Gilbert. 1991. Simulating speech systems. Computer Speech and Language 5 (1991), 81–99.Google ScholarGoogle ScholarCross RefCross Ref
  16. Paul Green and L. Wei-Haas. 1985. The Wizard of Oz: A Tool for Rapid development of User Interfaces. Report No. UMTRI-85-27. University of Michigan, Transportation Research Institute, Ann Arbor, USA.Google ScholarGoogle Scholar
  17. Franziska Hartwich, Matthias Beggiato, and Josef F. Krems. 2018. Driving comfort, enjoyment and acceptance of automated driving–effects of drivers’ age and driving style familiarity. Ergonomics 61, 7 (Feb. 2018), 1017–1032. https://doi.org/10.1080/00140139.2018.1441448Google ScholarGoogle ScholarCross RefCross Ref
  18. A. Hamish Jamson, Daryl L. Hibberd, and Natasha Merat. 2015. Interface design considerations for an in-vehicle eco-driving assistance system. Transportation Research Part C: Emerging Technologies 58 (Sept. 2015), 642–656. https://doi.org/10.1016/j.trc.2014.12.008Google ScholarGoogle Scholar
  19. Oliver Jarosch, Svenja Paradies, Daniel Feiner, and Klaus Bengler. 2019. Effects of non-driving related tasks in prolonged conditional automated driving – A Wizard of Oz on-road approach in real traffic environment. Transportation Research Part F: Traffic Psychology and Behaviour 65 (Aug. 2019), 292–305. https://doi.org/10.1016/j.trf.2019.07.023Google ScholarGoogle Scholar
  20. Wendy Ju. 2015. The Design of Implicit Interactions. Synthesis Lectures on Human-Centered Informatics (2015).Google ScholarGoogle Scholar
  21. Wen-Tai Lai. 2015. The effects of eco-driving motivation, knowledge and reward intervention on fuel efficiency. Transportation Research Part D: Transport and Environment 34 (Jan. 2015), 155–160. https://doi.org/10.1016/j.trd.2014.10.003Google ScholarGoogle Scholar
  22. Philipp Mayring. 2010. Qualitative Inhaltsanalyse. In Handbuch Qualitative Forschung in der Psychologie, Günther Mey and Katja Mruck (Eds.). VS Verlag für Sozialwissenschaften, Wiesbaden, 601–613.Google ScholarGoogle Scholar
  23. Andrea Isabell Müller, Veronika Weinbeer, and Klaus Bengler. 2019. Using the Wizard of Oz Paradigm to Prototype Automated Vehicles: Methodological Challenges. In Adjunct Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM Press, New York, NY, 181–186. https://doi.org/10.1145/3349263.3351526Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Frederik Naujoks, Christian Purucker, Katharina Wiedemann, and Claus Marberger. 2019. Noncritical State Transitions During Conditionally Automated Driving on German Freeways: Effects of Non–Driving Related Tasks on Takeover Time and Takeover Quality. Human Factors 61, 4 (Jan. 2019), 596–613. https://doi.org/10.1177/0018720818824002Google ScholarGoogle ScholarCross RefCross Ref
  25. Kamil Omozik, Yucheng Yang, Isabella Kuntermann, Sebastian Hergeth, and Klaus Bengler. 2019. How long does it take to relax? Observation of driver behaviour during real-world conditionally automated driving. In Proceedings of the Tenth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design. 245–251.Google ScholarGoogle ScholarCross RefCross Ref
  26. Sanna M. Pampel, Samantha L. Jamson, Daryl L. Hibberd, and Yvonne Barnard. 2015. How I reduce fuel consumption: An experimental study on mental models of eco-driving. Transportation Research Part C: Emerging Technologies 58 (Sept. 2015), 669–680. https://doi.org/10.1016/j.trc.2015.02.005Google ScholarGoogle Scholar
  27. Ingrid Pettersson and Wendy Ju. 2017. Design Techniques for Exploring Automotive Interaction in the Drive towards Automation. In Proceedings of the 2017 Conference on Designing Interactive Systems, Oli Mival (Ed.). ACM Press, New York, NY, 147–160. https://doi.org/10.1145/3064663.3064666Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Jonas Radlmayr, Thomas Selzer, Antonio Arcati, and Klaus Bengler. 2015. Haptic Gear Shifting Indication: Evaluating Acceptance and Potential Fuel Consumption Reduction. Procedia Manufacturing 3(2015), 2746–2752. https://doi.org/10.1016/j.promfg.2015.07.694Google ScholarGoogle ScholarCross RefCross Ref
  29. Mark J.M. Sullman, Lisa Dorn, and Pirita Niemi. 2015. Eco-driving training of professional bus drivers – Does it work?Transportation Research Part C: Emerging Technologies 58 (Sept. 2015), 749–759. https://doi.org/10.1016/j.trc.2015.04.010Google ScholarGoogle Scholar
  30. Mark Turner and Michael J. Griffin. 1999. Motion sickness in public road transport: The relative importance of motion, vision and individual differences. British Journal of Psychology 90 (Nov. 1999), 519–530. https://doi.org/10.1348/000712699161594Google ScholarGoogle Scholar
  31. Mascha van der Voort, Mark S. Dougherty, and Martin van Maarseveen. 2000. A prototype fuel-efficiency support tool. Transportation Research Part C: Emerging Technologies 9 (Aug. 2000), 279–296. https://doi.org/10.1016/S0968-090X(00)00038-3Google ScholarGoogle Scholar
  32. Peter Wang, Srinath Sibi, Brian Mok, and Wendy Ju. 2017. Marionette: Enabling On-Road Wizard-of-Oz Autonomous Driving Studies. In Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, Bilge Mutlu and M. Tscheligi (Eds.). ACM Press, New York, NY, 234–243. https://doi.org/10.1145/2909824.3020256Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Veronika Weinbeer, Christoph Baur, Jonas Radlmayr, Julian-Sebastian Bill, Tobias Muhr, and Klaus Bengler. 2017. Highly automated driving: How to get the driver drowsy and how does drowsiness influence various take-over aspects?. In 8. Tagung Fahrerassistenz, Einführung hochautomatisiertes Fahren.Google ScholarGoogle Scholar
  34. Veronika Weinbeer, Julian-Sebastian Bill, Christoph Baur, and Klaus Bengler. 2018. Automated driving: subjective assessment of different strategies to manage drowsiness. In Proceedings of the Human Factors and Ergononomics Society Europe Chapter 2017 Annual Conference. 5–17.Google ScholarGoogle Scholar
  35. Veronika Weinbeer, Tobias Muhr, and Klaus Bengler. 2018. Automated Driving: The Potential of Non-driving Related Tasks to Manage Driver Drowsiness. In Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018), Sebastiano Bagnara, R. Tartaglia, Sara Albolino, Thomas Alexander, and Yushi Fujita(Eds.). Springer, Cham, 179–188. https://doi.org/10.1007/978-3-319-96074-6_19Google ScholarGoogle Scholar
  36. Alf Zimmer. 2002. Über die Ergonomie hinaus: Neue Wege zu einer menschgerechten Technikgestaltung.Google ScholarGoogle Scholar

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        cover image ACM Other conferences
        NordiCHI '20: Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society
        October 2020
        1177 pages
        ISBN:9781450375795
        DOI:10.1145/3419249

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        • Published: 26 October 2020

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