Learning to use automation: Behavioral changes in interaction with automated driving systems

Transportation Research Part F: Traffic Psychology and Behaviour

Volumn 62, Issue , 2019, Pages 599-614

  Forster, Yannick ^a,b   Hergeth, Sebastian ^a   Naujoks, Frederik ^a   Beggiato, Matthias ^b   Krems, Josef F ^b   Keinath, Andreas ^a  

^a BMW GROUP   (Germany)

^b CHEMNITZ UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

Automated driving; Behavioral measures; Human machine interface; Method development; Power law of learning

Indexed keywords

BEHAVIORAL RESEARCH; HUMAN REACTION TIME; MAN MACHINE SYSTEMS;

AUTOMATED DRIVING; BEHAVIORAL MEASURES; HUMAN MACHINE INTERFACE; METHOD DEVELOPMENT; POWER-LAW;

AUTOMATION;

EID: 85062280762     PISSN: 13698478     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trf.2019.02.013     Document Type: Article

References (58)

1. AAM, Statement of principles, criteria and verification procedures on driver interactions with advanced in-vehicle information and communication systems. 2006, Alliance of Automobile Manufactures, Washington, DC.
2. Abraham, H., Seppelt, B., Mehler, B., & Reimer, B. (2017). What's in a name: Vehicle technology branding & consumer expectations for automation. In Boll, Pfleging et al. (Hg.) – the 9th international conference on automotive user interfaces and vehicular applications (pp. 226–234). https://doi.org/10.1145/3122986.3123018.
3. Amelang, M., Schmidt-Atzert, L., Psychologische Diagnostik und Intervention. 2006, Springer.
4. Andre, A.D., Wickens, C.D., When users want what's not best for them. Ergonomics in Design 3 (1995), 10–14.
5. Beggiato, M., Krems, Josef F., The evolution of mental model, trust and acceptance of adaptive cruise control in relation to initial information. Transportation Research Part F: Traffic Psychology and Behaviour 18 (2013), 47–57.
6. Beggiato, M., Pereira, M., Petzoldt, T., Krems, Josef, Learning and development of trust, acceptance and the mental model of ACC. A longitudinal on-road study. Transportation Research Part F: Traffic Psychology and Behaviour 35 (2015), 75–84, 10.1016/j.trf.2015.10.005.
7. Brooke, J., SUS - A quick and dirty usability scale. Usability Evaluation in Industry, 194, 1996.
8. Bühner, M., Einführung in die Test-und Fragebogenkonstruktion. 2011, Pearson Deutschland GmbH.
9. Carsten, O., Kircher, K., Jamson, S., Vehicle-based studies of driving in the real world: The hard truth?. Accident Analysis & Prevention 58 (2013), 162–174.
10. DIN-EN ISO (2011). Ergonomie der Mensch-System-Interaktion – Teil 210: Prozess zur Gestaltung gebrauchstauglicher interaktiver Systeme. (DIN-EN, 9241-210).
11. Forster, Y., Hergeth, S., Naujoks, F., Beggiato, M., Krems, Josef F., & Keinath, A. (2019). Learning and development of mental models in interaction with driving automation: A simulator study. Driving assessment conference, Santa Fe, NM, USA.
12. Forster, Y., Hergeth, S., Naujoks, F., Krems, Josef F., How usability can save the day: Methodological considerations for making automated driving a success story. Donmez, B., Walker, B.N., Fröhlich, K., (eds.) Proceedings of the 10th international conference on automotive user interfaces and interactive vehicular applications, 2018.
13. Forster, Y., Hergeth, S., Naujoks, F., Krems, Josef F., & Keinath, A. (2018). Self-report measures for the evaluation of human-machine interfaces in automated driving. Submitted and Under Review.
14. Forster, Y., Naujoks, F., Neukum, A., Your turn or my turn? Design of a human-machine interface for conditional automation. Proceedings of the 8th international conference on automotive user interfaces and interactive vehicular applications, 2016, 253–260, 10.1145/3003715.3005463.
15. Hayhoe, M., Ballard, D., Eye movements in natural behavior. Trends in Cognitive Sciences 9 (2005), 188–194, 10.1016/j.tics.2005.02.009.
16. Hergeth, S. (2016). Automation trust in conditional automated driving systems: Approaches to operationalization and design. PhD Thesis.
17. Hergeth, S., Lorenz, L., Krems, Josef F., Prior familiarization with takeover requests affects drivers’ takeover performance and automation trust. Human Factors 59 (2017), 457–470.
18. Hergeth, S., Lorenz, L., Vilimek, R., Krems, Josef F., Keep your scanners peeled: Gaze behavior as a measure of automation trust during highly automated driving. Human Factors 58 (2016), 509–519.
19. Howell, D.C., The treatment of missing data. The Sage Handbook of Social Science Methodology, 2007, 208–224.
20. Huys, R., Daffertshofer, A., Beek, P.J., Multiple time scales and multiform dynamics in learning to juggle. Motor Control 8 (2004), 188–212, 10.1123/mcj.8.2.188.
21. ISO (2012). Road vehicles—Ergonomic aspects of transport information and control systems—Calibration tasks for methods which assess driver demand due to the use of in-vehicle systems. (ISO, 14198). Geneva, Switzerland.
22. Jahn, G., Krems, Josef F., Gelau, C., Skill acquisition while operating in-vehicle information systems: Interface design determines the level of safety-relevant distractions. Human Factors 51 (2009), 136–151, 10.1177/0018720809336542.
23. Jarosch, O., Bengler, K., Rating of take-over performance in conditionally automated driving using an expert-rating system. International Conference on Applied Human Factors and Ergonomics, Los Angeles, CA, USA, 2018.
24. Kauffmann, N., Winkler, F., Vollrath, M., What Makes an Automated Vehicle a Good Driver?. Proceedings of the 2018 CHI conference on human factors in computing systems, Montreal, 2018.
25. Kaye, S.-A., Lewis, I., Freeman, J., Comparison of self-report and objective measures of driving behavior and road safety: A systematic review. Journal of Safety Research 65 (2018), 141–151, 10.1016/j.jsr.2018.02.012.
26. Kenntner-Mabiala, R., Kaussner, Y., Hoffmann, S., Volk, M., Driving performance of elderly drivers in comparison to middle-aged drivers during a representative, standardized driving test. Zeitschrift Für Verkehrsscherheit, 3, 2016, 73.
27. Konradt, U., Christophersen, T., Measuring psychological constructs using single-item scales: Answers to experts’ comments and additional questions. Interacting with Computers 25 (2013), 331–333.
28. Lewis, J., Psychometric evaluation of the PSSUQ using data from five years of usability studies. International Journal of Human-Computer Interaction 14 (2002), 463–488, 10.1515/9783110887242.3.
29. Lewis, J., Sauro, J., The factor structure of the system usability scale. International conference on human centered design, 2009.
30. Lord, F.M., On the statistical treatment of football numbers. American Psychologist 8 (1953), 750–751.
31. Louw, T., Madigan, R., Carsten, O., Merat, N., Were they in the loop during automated driving? Links between visual attention and crash potential. Injury Prevention 23 (2016), 281–286.
32. Lüdtke, O., Robitzsch, A., Trautwein, U., Köller, O., Umgang mit fehlenden Werten in der psychologischen Forschung. Psychologische Rundschau 58 (2007), 103–117, 10.1026/0033-3042.58.2.103.
33. Manca, L., de Winter, J.C.F., Happee, R., Visual displays for autoamted driving: A survey. Proceedings of the 7th international conference on automotive user interfaces and interactive vehicular applications, Nottingham, 2015.
34. Mehlenbacher, B., Wogalter, M.S., Laughery, K.R., On the reading of product owner's manuals: Perceptions and product complexity. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46 (2002), 730–734, 10.1177/154193120204600610.
35. Michon, J.A., A critical view of driver behavior models: What do we know, what should we do?. Human behavior and traffic safety, 1985, Springer, 485–524.
36. Naujoks, F., Hergeth, S., Keinath, A., Wiedemann, K., Schömig, N., Use cases for assessing, testing, and validating the human machine interface of automated driving systems. 2018, Human Factors and Ergonomics Society Annual Meeting, Philadelphia, PA, USA.
37. Naujoks, F., Purucker, C., Neukum, A., Wolter, S., Steiger, R., 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 35 (2015), 185–198.
38. Naujoks, F., Wiedemann, K., Schömig, N., Hergeth, S., Keinath, A., Towards guidelines and verification methods for automated vehicle HMIs. Transportation Research Part F: Traffic Psychology and Behaviour 60 (2019), 121–136.
39. Naujoks, F., Wiedemann, K., Schömig, N., Jarosch, O., Gold, C., Expert-based controllability assessment of control transitions from automated to manual driving. MethodsX 5 (2018), 579–592, 10.1016/j.mex.2018.05.007.
40. Neukum, A., Lübbecke, T., Krüger, H.-P., Mayser, C., Steinle, J., ACC-Stop&Go: Fahrerverhalten an funktionalen Systemgrenzen. 2008, Workshop Fahrerassistenzsysteme-FAS, 5.
41. Neves, D.M., Anderson, J.R., Knowledge compilation: Mechanisms for the automatization of cognitive skills. Cognitive Skills and Their Acquisition, 1981, 57–84.
42. Newell, A., Rosenbloom, P.S., Mechanisms of skill acquisition and the law of practice. Cognitive Skills and Their Acquisition 1 (1981), 1–55.
43. NHTSA, Federal automated vehicles policy. 2016, Accelerating the Next Revolution in Roadway Safety, NHTSA, US DOT.
44. Nielsen, J., Levy, J., Measuring usability: Preference vs. performance. Communications of the ACM 37 (1994), 66–75.
45. Norman, G., Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education: Theory and Practice 15 (2010), 625–632, 10.1007/s10459-010-9222-y.
46. Nunnally, J.C., Psychometric theory. 1978, McGraw-Hill, New York.
47. Palmer, C., Meyer, R.K., Conceptual and motor learning in music performance. Psychological Science 11 (2000), 63–68, 10.1111/1467-9280.00216.
48. Payre, W., Cestac, J., Dang, N.-T., Vienne, F., Delhomme, P., Impact of training and in-vehicle task performance on manual control recovery in an automated car. Transportation Research Part F: Traffic Psychology and Behaviour 46 (2017), 216–227, 10.1016/j.trf.2017.02.001.
49. Pettersson, I., Lachner, F., Frison, A.-K., Riener, A., Butz, A., A Bermuda triangle?: A review of method application and triangulation in user experience evaluation. Mandryk, R., Hancock, M., Perry, M., Cox, A., (eds.) The 2018 CHI Conference, 2018, 1–16, 10.1145/3173574.3174035.
50. Plan, E.L., Modeling and simulation of count data. CPT Pharmacothentrics & Systems Pharmacology 3 (2014), 1–12.
51. Ross, L., Lepper, M.R., Hubbard, M., Perseverance in self-perception and social perception: Biased attributional processes in the debriefing paradigm. Journal of Personality and Social Psychology, 32, 1975, 880.
52. SAE. (2018). Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems (No. J3016R).
53. Sullivan, G.M., Feinn, R., Using effect size-or why the p value is not enough. Journal of Graduate Medical Education 4 (2012), 279–282, 10.4300/JGME-D-12-00156.1.
54. Tabachnick, B.G., Fidell, L.S., Using multivariate statistics. 2007, Allyn & Bacon/Pearson Education.
55. Totzke, I. (2012). Einfluss des Lernprozesses auf den Umgang mit menügesteuerten Fahrerinformationssystemen. Doctoral Dissertation, University of Wuerzburg.
56. Vandekerckhove, J., Matzke, D., Wagenmakers, E.-J., Model comparison and the principle. The Oxford Handbook of Computational and Mathematical Psychology, 2015, 300.
57. Wickens, C.D., Hollands, J.G., Banbury, S., Parasuraman, R., Engineering psychology & human performance. 2015, Psychology Press.
58. Winkler, S., Kazazi, J., Vollrath, M., Practice makes better–Learning effects of driving with a multi-stage collision warning. Accident Analysis & Prevention 117 (2018), 398–409.