EFFECTS OF AUGMENTED SITUATIONAL AWARENESS ON DRIVER TRUST IN SEMI-AUTONOMOUS VEHICLE OPERATION
Abstract
Although autonomy has the potential to help military drivers travel safely while performing other tasks, many drivers refuse to rely on the technology. Military drivers sometimes fail to leverage a vehicle’s autonomy because of a lack of trust. To address this issue, the current study examines whether augmenting the driver’s situational awareness will promote their trust in the autonomy. Results of this study are expected to provide new insights into promoting trust and acceptance of autonomy in military settings.
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Petersen et al. focused on trying to increase passengers' trust through augmentation of their situational awareness and consideration of actual applications of semi-autonomous vehicles.
“Petersen et al. [56] specifically focused on trying to increase passengers' trust through augmentation of their situational awareness and consideration of actual applications of semi-autonomous vehicles.”
From Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2024) · cites this paper as [56]
Cites
MUIR, B. M., & MORAY, N. (1996) · Ergonomics · doi:10.1080/00140139608964474 · Cited by 993 · see citing works
Individual Swift Trust and Knowledge-Based Trust in Face-to-Face and Virtual Team Members
Robert, L. P., Denis, A. R., & Hung, Y.-T. C. (2009) · Journal of Management Information Systems · doi:10.2753/mis0742-1222260210 · Cited by 514 · see citing works
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Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10179-8 · Cited by 2,062 · see citing works
“Petersen et al. [56] specifically focused on trying to increase passengers’ trust through augmentation of their situational awareness and consideration of actual applications of semi-autonomous vehicles.”
BackgroundA Survey of Data-driven and Knowledge-aware eXplainable AI
Li, X.-H., Cao, C. C., Shi, Y., Bai, W., Gao, H., Qiu, L., Wang, C., Gao, Y., Zhang, S., Xue, X., et al. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2983930 · Cited by 224 · see citing works
- Authors
- Petersen, L., Tilbury, D., Robert, L., & Yang, X. J.
- Affiliations
- University of Michigan (US)
- Venue
- SAE Technical Paper Series
- Published
- 2017-08-08
- Volume
- 1
- ISSN
- 0148-7191 (ISSN-L)
- 2688-3627
- OpenAlex
- W2740560229
- Cited by
- 18
- References
- 2
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1 · 2022: 2 · 2021: 3 · 2020: 4 · 2019: 2 · 2018: 4
- FWCI
- 4.43
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- SAE technical papers on CD-ROM/SAE technical paper series (publishedVersion)
- Deep Blue (University of Michigan) (acceptedVersion)
- 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
- driver trust, situational awareness, semi-autonomous vehicles, military vehicle drivers, driver-vehicle interaction, technology acceptance
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