The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Do We Blindly Trust Self-Driving Cars
Abstract
Trust is an essential factor in ensuring robust human-robot interaction. However, recent work suggests that people can be too trusting of the technology with which they interact during emergencies, causing potential harm to themselves. To test whether this "over-trust" also extends to normal day-to-day activities, such as driving a car, we carried out a series of experiments with an autonomous car simulator. Participants (N=73) engaged in a scenario with no, correct or false audible information regarding the state of traffic around the self-driving vehicle, and were told they could assume control at any point in the interaction. Results show that participants trust the autonomous system, even when they should not, leading to potential dangerous situations.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“Ribeiro et al. [110] found that displaying class probabilities did not help users to appropriately assess a system's trustworthiness (see also [6]).”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [6]
Citations
Cites
The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle
Waytz, A., Heafner, J., & Epley, N. (2014) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2014.01.005 · Cited by 1,439
Koo, J., Kwac, J., Ju, W. G., Steinert, M., LEIFER, L. J., & Nass, C. (2014) · International Journal on Interactive Design and Manufacturing (IJIDeM) · doi:10.1007/s12008-014-0227-2 · Cited by 495
Overtrust of robots in emergency evacuation scenarios
Robinette, P., Li, W., Allen, R., Howard, A. M., & Wagner, A. R. (2016) · 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2016.7451740 · Cited by 468
Helldin, T., Falkman, G., Riveiro, M., & Davidsson, S. (2013) · Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications · doi:10.1145/2516540.2516554 · Cited by 286 · cited 1×
“Work by Helldin et al. [3] suggests that drivers, who receive information about an SDCs uncertainty on how to act in a given situation, tend to trust the car less than drivers who did not receive such information.”
BackgroundTo delegate or not to delegate: A review of control frameworks for autonomous cars
Richards, D., & Stedmon, A. W. (2015) · Applied Ergonomics · doi:10.1016/j.apergo.2015.10.011 · Cited by 54
Research works printed but not linked: 1
- 10.5555/2906831.2906851 · DOI
Cited by
Sorted by citations
Wischnewski, M., Krämer, N., & Müller, E. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581197 · Cited by 146
“Ribeiro et al. [110] found that displaying class probabilities did not help users to appropriately assess a system’s trustworthiness (see also [6]).”
BackgroundEnhancing Trust in Autonomous Vehicles through Intelligent User Interfaces That Mimic Human Behavior
Ruijten, P. A. M., Terken, J. M. B., & Chandramouli, S. N. (2018) · Multimodal Technologies and Interaction · doi:10.3390/mti2040062 · Cited by 128
Trust and Distrust of Automated Parking in a Tesla Model X
Tenhundfeld, N. L., de Visser, E. J., Ries, A. J., Finomore, V. S., & Tossell, C. C. (2019) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720819865412 · Cited by 83
Not Only WEIRD but “Uncanny”? A Systematic Review of Diversity in Human–Robot Interaction Research
Seaborn, K., Barbareschi, G., & Chandra, S. (2023) · International Journal of Social Robotics · doi:10.1007/s12369-023-00968-4 · Cited by 64
AI Accidents: An Emerging Threat
Arnold, Z., & Toner, H. (2021) · doi:10.51593/20200072 · Cited by 15
Should Conditional Self-Driving Cars Consider the State of the Human Inside the Vehicle?
Puertas-Ramirez, D., Serrano-Mamolar, A., Martín, D., & Boticario, J. G. (2021) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3450614.3462243 · Cited by 13
Is my User Impaired? Designing Adaptive Automation that Monitors the User’s State
Cotter, J. E., Atchley, A., Banz, B., & Tenhundfeld, N. (2021) · Systems and Information Engineering Design Symposium · doi:10.1109/sieds52267.2021.9483731 · Cited by 8
Simulations and Self-Driving Cars
Pedersen, B. K. M. K., Andersen, K. E., Köslich, S., Weigelin, B. C., & Kuusinen, K. (2018) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3173386.3176987 · Cited by 7
Self-citationHeuristics to Design Trustworthy Technologies: Study Design and Current Progress
Paramonova, I., Sousa, S., & Lamas, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42293-5_60 · Cited by 4
Metadata
- Authors
- Andersen, K. E., Köslich, S., Pedersen, B. K. M. K., Weigelin, B. C., & Jensen, L. C.
- Affiliations
- University of Southern Denmark (DK)
- Venue
- Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction
- Publisher
- ACM
- Published
- 2017-03-06
- Pages
- 67-68
- OpenAlex
- W2593665726
- Cited by
- 9
- References
- 5
- Citations by year
- 2023: 3 · 2021: 3 · 2019: 1 · 2018: 2
- FWCI
- 1.33
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM/IEEE International Conference on Human-Robot Interaction (HRI) (publishedVersion)
- University of Southern Denmark Research Portal (University of Southern Denmark) (publishedVersion)
- Topics
- Social Sciences › Human Factors and Ergonomics › 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
- trust in autonomous systems, autonomous vehicles, human-robot interaction, emergency situations
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
