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Journal of Advanced Transportation
Volume 2017, Article ID 6105087, 12 pages
https://doi.org/10.1155/2017/6105087
Research Article

Improving Usefulness of Automated Driving by Lowering Primary Task Interference through HMI Design

1Center for Traffic Sciences (IZVW), University of Würzburg, Würzburg, Germany
2Würzburg Institute for Traffic Sciences (WIVW), Veitshöchheim, Germany

Correspondence should be addressed to Frederik Naujoks; ed.wviw@skojuan

Received 13 April 2017; Revised 4 June 2017; Accepted 28 June 2017; Published 16 August 2017

Academic Editor: David F. Llorca

Copyright © 2017 Frederik Naujoks et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. SAE, “Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems,” in Proceedings of the, Washington, DC, 2014.
  2. R. A. Daziano, M. Sarrias, and B. Leard, “Are consumers willing to pay to let cars drive for them? Analyzing response to autonomous vehicles,” Transportation Research Part C: Emerging Technologies, vol. 78, no. Part C, pp. 150–164, 2017. View at Publisher · View at Google Scholar
  3. C. J. Haboucha, R. Ishaq, and Y. Shiftan, “User preferences regarding autonomous vehicles,” Transportation Research Part C: Emerging Technologies, vol. 78, no. Part C, pp. 37–49, 2017, http://doi.org/10.1016/j.trc.2017.01.010doi. View at Publisher · View at Google Scholar
  4. M. K, L. Neumayr, and M. König, “Users resistance towards radical innovations: The case of the self-driving car,” Transportation research part F: traffic psychology and behaviour, vol. 44, no. part F, pp. 42–52, 2017. View at Publisher · View at Google Scholar
  5. K. J. Shin and S. Managi, Consumer Demand for Fully Automated Driving Technology: Evidence from Japan: Research Institute of Economy, Trade and Industry (RIETI,.
  6. K. Mathieson, “Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior,” Information Systems Research, vol. 2, no. 3, pp. 173–191, 1991. View at Publisher · View at Google Scholar · View at Scopus
  7. V. Venkatesh and F. D. Davis, “A theoretical extension of the technology acceptance model: four longitudinal field studies,” Management Science, vol. 46, no. 2, pp. 186–204, 2000. View at Publisher · View at Google Scholar · View at Scopus
  8. F. Naujoks, C. Purucker, and A. Neukum, Secondary task engagement and vehicle automationComparing the effects of different automation levels in an on-road experiment, vol. 38 of Transportation research part F: traffic psychology and behaviour, 2016. View at Publisher · View at Google Scholar
  9. B. Pfleging, M. Rang, and N. Broy, “Investigating user needs for non-driving-related activities during automated driving,” in Proceedings of the 15th International Conference on Mobile and Ubiquitous Multimedia Rovaniemi, Rovaniemi, Finland, 2016.
  10. B. Schoettle and M. Sivak, Public opinion about self-driving vehicles in, UK, and Australia., Michigan, Transportation Research Institute, University of Michigan (UMTRI, China, India, Japan, the US, the, 2014.
  11. M. Beggiato, F. Hartwich, K. Schleinitz, J. Krems, I. Othersen, and I. Petermann-Stock, “What would drivers like to know during automated driving? Information needs at different levels of automation,” in Proceedings of the 7th Conference on Driver Assistance, 2015.
  12. J. Beller, M. Heesen, and M. Vollrath, “Improving the driverautomation interaction an approach using automation uncertainty,” Human Factors, vol. 55, no. 6, pp. 1130–1141, 2013. View at Publisher · View at Google Scholar
  13. J. C. F. De Winter, N. A. Stanton, J. S. Price, and H. Mistry, “The effects of driving with different levels of unreliable automation on self-reported workload and secondary task performance,” International Journal of Vehicle Design, vol. 70, no. 4, pp. 297–324, 2016. View at Publisher · View at Google Scholar · View at Scopus
  14. T. Helldin, G. Falkman, M. Riveiro, and S. Davidsson, “Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving,” in Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2013, J. Terken, M. H. Martens, C. Müller, J. Healey, and S. Osswald, Eds., pp. 210–217, New York, NY, USA, October 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. C. Gold, F. Naujoks, J. Radlmayr, H. Bellem, and O. Jarosch, “Testing-scenarios for human factors research in highly automated vehicles,” in Proceedings of the the 8th International Conference on Applied Human Factors and Ergonomics, Los Angeles, LA, USA, 2017.
  16. S. S. Borojeni, L. Chuang, W. Heuten, and S. Boll, “Assisting drivers with ambient take-over requests in highly automated driving,” in Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, P. Green, S. Boll, G. Burnett, J. Gabbard, and S. Osswald, Eds., pp. 237–244, New York, NY, USA, 2016.
  17. C. Gold, D. Damböck, L. Lorenz, and K. Bengler, “Take over! How long does it take to get the driver back into the loop?” in Proceedings of the 57th Human Factors and Ergonomics Society Annual Meeting - 2013, HFES 2013, pp. 1938–1942, usa, October 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. B. K.-J. Mok, M. Johns, K. J. Lee, H. P. Ive, D. Miller, and W. Ju, “Timing of unstructured transitions of control in automated driving,” in Proceedings of the Intelligent Vehicles Symposium (IV), 2015.
  19. F. Naujoks, C. Mai, and A. Neukum, “The effect of urgency of take-over requests during highly automated driving under distraction conditions,” in Proceedings of the 5th AHFE Conference, T. Ahram, W. Karowski, and T. Marek, Eds., vol. 7, pp. 2099–2106, Krakau, Poland, 2014.
  20. F. Naujoks, C. Purucker, A. Neukum, S. Wolter, and R. Steiger, “Controllability of partially automated driving functions - does it matter whether drivers are allowed to take their hands off the steering wheel?” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 35, pp. 185–198, 2015. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Petermeijer, P. Bazilinskyy, K. Bengler, and J. de Winter, “Take-over again: Investigating multimodal and directional TORs to get the driver back into the loop,” Applied Ergonomics, vol. 62, pp. 204–215, 2017. View at Publisher · View at Google Scholar
  22. M. Walch, K. Mühl, M. Baumann, M. Weber, and K. Mühl, “Autonomous Driving: Investigating the Feasibility of Bimodal Take-Over Requests,” in Proceedings of the International Journal of Mobile Human Computer Interaction (IJMHCI, vol. 9, pp. 58–74, 2017.
  23. K. Zeeb, A. Buchner, and M. Schrauf, “Is take-over time all that matters? the impact of visual-cognitive load on driver take-over quality after conditionally automated driving,” Accident Analysis and Prevention, vol. 92, pp. 230–239, 2016. View at Publisher · View at Google Scholar · View at Scopus
  24. A. Eriksson and N. Stanton, “Take-over time in highly automated vehicles: non-critical transitions to and from manual control,” Human Factors, 2017. View at Publisher · View at Google Scholar
  25. A. Larsson, “A Countdown to Manual Driving: How Do Drivers Get “Back-in-the-Loop”?” in Advances in Human Aspects of Transportation, vol. 484 of Advances in Intelligent Systems and Computing, pp. 463–471, Springer International Publishing, Cham, 2017. View at Publisher · View at Google Scholar
  26. D. Miller, A. Sun, M. Johns et al., “Distraction becomes engagement in automated driving,” in Proceedings of the 59th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2014, pp. 1676–1680, usa, October 2015. View at Publisher · View at Google Scholar · View at Scopus
  27. F. Naujoks, Y. Forster, K. Wiedemann, and A. Neukum, “a). A Human-Machine Interface for Cooperative Highly Automated Driving,” in Proceedings of the Paper presented at the 7th AHFE Conference, Orlando, 2016.
  28. K. Wiedemann, N. Sch, C. Mai, F. Naujoks, A. Neukum, and N. Schömig, “Drivers' monitoring behaviour and interaction with non-driving related tasks during driving with different automation levels,” in Proceedings of the Paper presented at the 6th AHFE Conference, Las Vegas, USA, 2015.
  29. R. Parasuraman and V. Riley, “Humans and automation: use, misuse, disuse, abuse,” Human Factors, vol. 39, no. 2, pp. 230–253, 1997. View at Publisher · View at Google Scholar · View at Scopus
  30. J. D. Lee and K. A. See, “Trust in automation: Designing for appropriate reliance,” Human Factors, vol. 46, no. 1, pp. 50–80, 2004. View at Publisher · View at Google Scholar · View at Scopus
  31. I. P. Tussyadiah, F. J. Zach, and J. Wang, Attitudes Toward Autonomous on Demand Mobility System: The Case of Self-Driving Taxi. Paper presented at the Information and Communication Technologies in Tourism, Attitudes Toward Autonomous on Demand Mobility System, The Case of Self-Driving Taxi. Paper presented at the Information and Communication Technologies in Tourism, 2017.
  32. M. Kyriakidis, J. C. de Winter, N. Stanton et al., “A human factors perspective on automated driving,” in Theoretical Issues in Ergonomics Science, p. 27, Theoretical Issues in Ergonomics Science, 2017. View at Google Scholar
  33. Y. Forster, F. Naujoks, and A. Neukum, “Your turn or my turn? design of a human-machine interface for conditional automation,” in Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, P. Green, S. Boll, G. Burnett, J. Gabbard, and S. Osswald, Eds., pp. 253–260, New York, NY, USA, 2016.
  34. M. Walch, T. Sieber, P. Hock, M. Baumann, and M. Weber, “Towards Cooperative Driving: Involving the Driver in an Autonomous Vehicle's Decision Making,” in Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, P. Green, S. Boll, G. Burnett, J. Gabbard, and S. Osswald, Eds., pp. 261–268, New York, 2016.
  35. F. Naujoks, K. Wiedemann, and N. Schömig, “The importance of interruption management for usefulness and acceptance of automated driving,” in in Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Oldenburg, Germany, 2017.
  36. T. Gillie and D. Broadbent, “What makes interruptions disruptive? A study of length, similarity, and complexity,” Psychological Research, vol. 50, no. 4, pp. 243–250, 1989. View at Publisher · View at Google Scholar · View at Scopus
  37. D. C. McFarlane and K. A. Latorella, “The scope and importance of human interruption in human-computer interaction design,” Human-Computer Interaction, vol. 17, no. 1, pp. 1–61, 2002. View at Publisher · View at Google Scholar · View at Scopus
  38. E. M. Altmann and J. G. Trafton, “Task Interruption: Resumption Lag and the Role of Cues,” in Proceedings of the Cognitive Science Society, K. Forbus, D. Genter, and T. Regier, Eds., vol. 26, pp. 43–49, Austin, TX, USA, 2004.
  39. M. B. Edwards and S. D. Gronlund, “Task interruption and its effects on memory,” Memory, vol. 6, no. 6, pp. 665–687, 1998. View at Publisher · View at Google Scholar · View at Scopus
  40. B. P. Bailey and J. A. Konstan, “On the need for attention-aware systems: measuring effects of interruption on task performance, error rate, and affective state,” Computers in Human Behavior, vol. 22, no. 4, pp. 685–708, 2006. View at Publisher · View at Google Scholar · View at Scopus
  41. Q. R. Jett and J. M. George, “Work interrupted: A closer look at the role of interruptions in organizational life,” Academy of Management Review, vol. 28, no. 3, pp. 494–507, 2003. View at Google Scholar · View at Scopus
  42. N. Rauch, A. Kaussner, H.-P. Krüger, S. Boverie, and F. Flemisch, “The importance of driver state assessment within highly automated vehicles,” in Proceedings of the 16th World Congress on Intelligent Transport Systems and Services, ITS 2009, swe, September 2009. View at Scopus
  43. N. Schömig, V. Hargutt, A. Neukum, I. Petermann-Stock, I. Othersen, and N. Schömig, “The interaction between highly automated driving and the development of drowsiness,” Procedia Manufacturing, vol. 3, pp. 6652–6659, 2015. View at Publisher · View at Google Scholar
  44. C. Marberger, H. Mielenz, F. Naujoks, J. Radlmayr, K. Bengler, and B. Wandtner, “Understanding and applying the concept of “driver availability” in automated driving,” in Proceedings of the 8th International Conference on Applied Human Factors and Ergonomics (AHFE 2017), Los Angeles, LA, USA, 2017.
  45. C. Neubauer, G. Matthews, and D. Saxby, “Fatigue in the automated vehicle: do games and conversation distract or energize the driver?” in Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 58, pp. 2053–2057, 2014.
  46. C. Ho, N. Reed, and C. Spence, “Multisensory in-car warning signals for collision avoidance,” Human Factors, vol. 49, no. 6, pp. 1107–1114, 2007. View at Publisher · View at Google Scholar · View at Scopus
  47. A. F. Kramer, N. Cassavaugh, W. J. Horrey, E. Becic, and J. L. Mayhugh, “Influence of age and proximity warning devices on collision avoidance in simulated driving,” Human Factors, vol. 49, no. 5, pp. 935–949, 2007. View at Publisher · View at Google Scholar · View at Scopus
  48. A. Kiesel and J. Miller, “Impact of contingency manipulations on accessory stimulus effects,” Perception and Psychophysics, vol. 69, no. 7, pp. 1117–1125, 2007. View at Publisher · View at Google Scholar · View at Scopus
  49. J. Miller, V. Franz, and R. Ulrich, “Effects of auditory stimulus intensity on response force in simple, go/no-go, and choice RT tasks,” Perception and Psychophysics, vol. 61, no. 1, pp. 107–119, 1999. View at Publisher · View at Google Scholar · View at Scopus
  50. J. L. Campbell, C. M. Richard, J. L. Brown, and M. McCallum, Crash warning system interfaces: human factors insights and lessons learned, vol. 810, Natinal Highway Traffic Safety Administration (NHTSA), Washintgon DC, USA, 2007.
  51. F. Naujoks, A. Kiesel, and A. Neukum, “Cooperative warning systems: the impact of false and unnecessary alarms on drivers' compliance,” Accident Analysis and Prevention, vol. 97, pp. 162–175, 2016. View at Publisher · View at Google Scholar · View at Scopus
  52. F. Naujoks and A. Neukum, Specificity and timing of advisory warnings based on cooperative perception, Mensch and Computer 2014 - Workshopband, De Gruyter Oldenbourg, Berlin, 2014.
  53. P. Bazilinskyy and J. de Winter, “Auditory interfaces in automated driving: an international survey,” PeerJ Computer Science, vol. 1, p. e13.
  54. Y. Forster, F. Naujoks, and A. Neukum, “Increasing Anthropomorphism and Trust in Automated Driving Functions by Adding Speech Output,” 2017.
  55. P. Fitts, R. Jones, and J. Milton, “Eye movements of aircraft pilots during instrument-landing approaches,” Aeronautical Engineering Review, vol. 9, no. 2, p. 1, 2005. View at Google Scholar
  56. R. Jacob and K. S. Karn, “Eye tracking in human-computer interaction and usability research: Ready to deliver the promises,” Mind, vol. 2, no. 3, p. 4, 2003. View at Google Scholar
  57. S. Hergeth, L. Lorenz, R. Vilimek, and J. F. Krems, “Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust during Highly Automated Driving,” Human Factors, vol. 58, no. 3, pp. 509–519, 2015. View at Publisher · View at Google Scholar · View at Scopus
  58. T. A. Dingus, S. G. Klauer, V. L. Neale et al., The 100-car naturalistic driving study, Phase II-results of the 100-car field experiment, National Highway Traffic Safety Administration, Washington DC, USA, 2006.
  59. J. Gilbert, Statistical Power Analysis for the Behavioral Sciences, Routledge Academic, New York, NY, USA, 1988. View at Publisher · View at Google Scholar
  60. J. Cohen, “A power primer,” Psychological Bulletin, vol. 112, no. 1, pp. 155–159, 1992. View at Publisher · View at Google Scholar · View at Scopus
  61. F. Naujoks, Y. Forster, K. Wiedemann, and A. Neukum, “Speech improves human-automation cooperation in automated driving,” in. Weyers, B. Dittmar, A. (Hrsg., Mensch und Computer, 2016. View at Google Scholar