Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving
Abstract
Objective: The feasibility of measuring drivers’ automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving simulator study. Background: Earlier research from other domains indicates that drivers’ automation trust might be inferred from gaze behavior, such as monitoring frequency. Method: The gaze behavior and self-reported automation trust of 35 participants attending to a visually demanding non-driving-related task (NDRT) during highly automated driving was evaluated. The relationship between dispositional, situational, and learned automation trust with gaze behavior was compared. Results: Overall, there was a consistent relationship between drivers’ automation trust and gaze behavior. Participants reporting higher automation trust tended to monitor the automation less frequently. Further analyses revealed that higher automation trust was associated with lower monitoring frequency of the automation during NDRTs, and an increase in trust over the experimental session was connected with a decrease in monitoring frequency. Conclusion: We suggest that (a) the current results indicate a negative relationship between drivers’ self-reported automation trust and monitoring frequency, (b) gaze behavior provides a more direct measure of automation trust than other behavioral measures, and (c) with further refinement, drivers’ automation trust during highly automated driving might be inferred from gaze behavior. Application: Potential applications of this research include the estimation of drivers’ automation trust and reliance during highly automated driving.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Trust in automation may cause humans to lose critical engagement with the task, so they lack the attention to intervene when necessary.
“Further, this trust may cause them to lose critical engagement with the task, so they do not have the attention to intervene when necessary, as has been shown in self-driving car examples[19].”
From Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative (2021) · cites this paper as [19]
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Construct validity in psychological tests.
Cronbach, L. J., & Meehl, P. E. (1955) · Psychological Bulletin · doi:10.1037/h0040957 · Cited by 11,195 · see citing works
Cohen, J. (1992) · Psychological Bulletin · doi:10.1037//0033-2909.112.1.155 · Cited by 8,771 · see citing works
Constructing validity: Basic issues in objective scale development.
Clark, L. A., & Watson, D. (1995) · Psychological Assessment · doi:10.1037/1040-3590.7.3.309 · Cited by 6,567 · see citing works
Humans and automation: Use, misuse, disuse, abuse
Parasuraman, R., & Riley, V. (1997) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872097778543886 · Cited by 4,554 · see citing works
TRUST AND DISTRUST IN ORGANIZATIONS: Emerging Perspectives, Enduring Questions
Kramer, R. M. (1999) · Annual Review of Psychology · doi:10.1146/annurev.psych.50.1.569 · Cited by 3,265 · see citing works
Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust
Hoff, K. A., & Bashir, M. (2015) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720814547570 · Cited by 2,817 · see citing works
Foundations for an Empirically Determined Scale of Trust in Automated Systems
Jian, J.-Y., Bisantz, A. M., & Drury, C. G. (2000) · International Journal of Cognitive Ergonomics · doi:10.1207/s15327566ijce0401_04 · Cited by 1,799 · see citing works
Complacency and Bias in Human Use of Automation: An Attentional Integration
Parasuraman, R., & Manzey, D. H. (2010) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720810376055 · Cited by 1,710 · cited 1× · see citing works
“In addition, Parasuraman and Manzey (2010) point out that overt behavioral indicators such as monitoring behavior might be connected more closely to subjective automation trust than other objective measures.”
BackgroundEye Tracking in Human-Computer Interaction and Usability Research
Jacob, R. J. K., & Karn, K. S. (2003) · Elsevier eBooks · doi:10.1016/b978-044451020-4/50031-1 · Cited by 1,575 · see citing works
MUIR, B. M., & MORAY, N. (1996) · Ergonomics · doi:10.1080/00140139608964474 · Cited by 993 · see citing works
“Take over!” How long does it take to get the driver back into the loop?
Gold, C., Damböck, D., Lorenz, L., & Bengler, K. J. (2013) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213571433 · Cited by 774 · see citing works
Self-citationEye tracking in HCI and usability research
Poole, A., & Ball, L. J. (2006) · Encyclopedia of Human Computer Interaction · doi:10.4018/978-1-59140-562-7.ch034 · Cited by 690 · cited 1× · see citing works
“Even so, eye trackers are sensitive instruments and will not always provide usable data, for example if there are interferences with eye-wear (Poole & Ball, 2006).”
BackgroundSheridan, T. B., & Parasuraman, R. (2005) · Reviews of Human Factors and Ergonomics · doi:10.1518/155723405783703082 · Cited by 479 · see citing works
Engineering Psychology and Human Performance
Vuuren, L. J. V. (1987) · SA Journal of Industrial Psychology · doi:10.4102/sajip.v13i1.457 · Cited by 388 · see citing works
Behavioural adaptation to adaptive cruise control (ACC): implications for preventive strategies
Rudin-Brown, C. M., & Parker, H. A. (2004) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2004.02.001 · Cited by 340 · see citing works
Beggiato, M., & Krems, J. F. (2013) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2012.12.006 · Cited by 328 · see citing works
Self-citationDefinition and Computation of Oculomotor Measures in the Study of Cognitive Processes
Inhoff, A. W., & Radach, R. R. (1998) · Elsevier eBooks · doi:10.1016/b978-008043361-5/50003-1 · Cited by 318 · see citing works
Experience, Results and Lessons Learned from Automated Driving on Germany's Highways
Aeberhard, M., Rauch, S., Bahram, M., Tanzmeister, G., Thomas, J., Pilat, Y., Homm, F., Huber, W., & Kaempchen, N. (2015) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2014.2360306 · Cited by 250 · see citing works
Secrecy, Trust, and Dangerous Leisure: Generating Group Cohesion in Voluntary Organizations
Fine, G. A., & Holyfield, L. (1996) · Social Psychology Quarterly · doi:10.2307/2787117 · Cited by 238 · see citing works
Llaneras, R. E., Salinger, J. A., & Green, C. A. (2013) · International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design (Driving Assessment Conference) · doi:10.17077/drivingassessment.1472 · Cited by 186 · see citing works
The Influence of Task Load and Automation Trust on Deception Detection
Biros, D. P., Daly, M., & Gunsch, G. H. (2004) · Group Decision and Negotiation · doi:10.1023/b:grup.0000021840.85686.57 · Cited by 120 · see citing works
Laboratory studies of trust between humans and machines in automated systems
Moray, N. P., & Inagaki, T. (1999) · Transactions of the Institute of Measurement and Control · doi:10.1177/014233129902100408 · Cited by 83 · see citing works
Saffarian, M., Happee, R., & de Winter, J. (2012) · Ergonomics · doi:10.1080/00140139.2012.691993 · Cited by 80 · see citing works
Hergeth, S., Lorenz, L., Krems, J. F., & Toenert, L. (2015) · International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design (Driving Assessment Conference) · doi:10.17077/drivingassessment.1591 · Cited by 74 · see citing works
Self-citationWarnings and Hazard Communications
Wogalter, M. S., Laughery, K. R., & Mayhorn, C. B. (2012) · doi:10.1002/9781118131350.ch29 · Cited by 60 · see citing works
DYNAMICS OF DRIVERS' TRUST IN WARNING SYSTEMS
Abe, G., Itor, M., & Tanaka, K. (2002) · IFAC Proceedings Volumes · doi:10.3182/20020721-6-es-1901.01614 · Cited by 26 · see citing works
Measurement of trust over time in hybrid inspection systems
Master, R., Jiang, X., Khasawneh, M. T., Bowling, S. R., Grimes, L. W., Gramopadhye, A. K., & Melloy, B. J. (2005) · Human Factors and Ergonomics in Manufacturing & Service Industries · doi:10.1002/hfm.20021 · Cited by 23 · see citing works
Brown, R. D., & Galster, S. M. (2004) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/154193120404800132 · Cited by 21 · see citing works
Measurement of Human Trust in a Hybrid Inspection for Varying Error Patterns
Madhani, K., Khasawneh, M. T., Kaewkuekool, S., Gramopadhye, A. K., & Melloy, B. J. (2002) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/154193120204600343 · Cited by 6 · see citing works
Behavioural Adaptation to In-Vehicle Safety Measures
Brown, C. M., & Noy, Y. I. (2004) · Elsevier eBooks · doi:10.1016/b978-008043925-9/50003-7 · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Introduction matters: Manipulating trust in automation and reliance in automated driving
Körber, M., Baseler, E., & Bengler, K. (2018) · Applied Ergonomics · doi:10.1016/j.apergo.2017.07.006 · Cited by 418 · see citing works
Theoretical Considerations and Development of a Questionnaire to Measure Trust in Automation
Körber, M. (2019) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-319-96074-6_2 · Cited by 317 · see citing works
Kraus, J., Scholz, D., Stiegemeier, D., & Baumann, M. (2020) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720819853686 · Cited by 276 · see citing works
“This is in line with earlier studies (e.g., Beggiato et al., 2015; Dzindolet et al., 2003; Hergeth et al., 2016; Lee & Moray, 1992).”
ResultAdaptive trust calibration for human-AI collaboration
Okamura, K., & Yamada, S. (2020) · PLOS ONE · doi:10.1371/journal.pone.0229132 · Cited by 239 · see citing works
At Your Service: Designing Voice Assistant Personalities to Improve Automotive User Interfaces
Braun, M., Mainz, A., Chadowitz, R., Pfleging, B., & Alt, F. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300270 · Cited by 182 · see citing works
SupportingTrust in Autonomous Driving
Häuslschmid, R., von Bülow, M., Pfleging, B., & Butz, A. (2017) · Proceedings of the 22nd International Conference on Intelligent User Interfaces · doi:10.1145/3025171.3025198 · Cited by 170 · see citing works
Kunze, A., Summerskill, S. J., Marshall, R., & Filtness, A. J. (2019) · Ergonomics · doi:10.1080/00140139.2018.1547842 · Cited by 165 · see citing works
Sebok, A., & Wickens, C. D. (2017) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720816665201 · Cited by 75 · see citing works
Levy, A., Agrawal, M., Satyanarayan, A., & Sontag, D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445522 · Cited by 73 · see citing works
Wintersberger, P., Riener, A., & Frison, A.-K. (2016) · Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications · doi:10.1145/3003715.3005410 · Cited by 55 · see citing works
The relationship of trust and dependence
Patton, C. E., & Wickens, C. D. (2024) · Ergonomics · doi:10.1080/00140139.2024.2342436 · Cited by 44 · see citing works
Trust Dynamics and Verbal Assurances in Human Robot Physical Collaboration
Alhaji, B., Prilla, M., & Rausch, A. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.703504 · Cited by 29 · see citing works
- Authors
- Hergeth, S., Lorenz, L., Vilimek, R., & Krems, J. F.
- Affiliations
- Chemnitz University of Technology (DE)
- BMW (Germany) (DE)
- Corresponding author
- Hergeth, Sebastian
- Venue
- Human Factors: The Journal of the Human Factors and Ergonomics Society
- Publisher
- SAGE Publications
- Published
- 2016-02-03
- Volume
- 58
- Issue
- 3
- Pages
- 509-519
- ISSN
- 0018-7208 (ISSN-L)
- 1547-8181
- OpenAlex
- W2319663855
- Semantic Scholar
- de011a4fd308a62ed9d19afcbac17d1f5d28be23
- Cited by
- 355
- References
- 32
- Citations by year
- 2026: 43 · 2025: 44 · 2024: 42 · 2023: 36 · 2022: 33 · 2021: 48 · 2020: 39 · 2019: 22 · 2018: 23 · 2017: 20 · 2016: 5
- FWCI
- 36.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Human Factors The Journal of the Human Factors and Ergonomics Society (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- gaze behavior, highly automated driving, monitoring frequency, eye tracking, non-driving related tasks, situational trust
- MeSH terms
- Adolescent, Adult, Automation, Automobile Driving, Eye Movements, Female, Humans, Male, Man-Machine Systems, Middle Aged, Trust, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
