No Risk No Trust: Investigating Perceived Risk in Highly Automated Driving
Abstract
When evaluating drivers' trust in automated systems, perceived risk is an inevitable, yet underestimated component, especially during initial interaction. We designed two experimental studies focusing on how people assess risk in different driving environments and how introductory information about automation reliability influences trust and risk perception. First, we designed nine driving scenarios to determine which factors influence Perceived Situational Risk (PSR) and Perceived Relational Risk (PRR). Results showed that participants identified levels of risk based on traffic type and vehicles' abnormal behaviors. We then evaluated how introductory information and situational risk influence trust and PRR. Results showed that participants reported the highest level of trust, perceived automation reliability, and the lowest level of PRR when presented with information about a highly reliable system, and when driving in a low-risk situation. These results highlight the importance of incorporating perceived risk and introductory information to support the trust calibration in automated vehicles.
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Subjective trust in the AI system was assessed using the Trust in Automation questionnaire (TiA), a commonly employed and validated tool for measuring trust.
“The subjective trust in the AI system was assessed using the Trust in Automation questionnaire (TiA) [63], which is a commonly employed and validated tool for measuring trust [78, 116, 120].”
From Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making (2024) · cites this paper as [78]
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Guruge, K., & Lee, S. C. (2026) · International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) · doi:10.1145/3828158.3838239
Arisoni, A., Leite, I., & Yadollahi, E. (2026) · IEEE RO-MAN · doi:10.1109/ro-man72286.2026.11714911
- Authors
- Li, M., Holthausen, B. E., Stuck, R. E., & Walker, B. N.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
- Publisher
- ACM
- Published
- 2019-09-21
- Pages
- 177-185
- OpenAlex
- W2973456219
- Cited by
- 80
- References
- 28
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 16 · 2023: 19 · 2022: 12 · 2021: 7 · 2020: 7
- FWCI
- 10.73
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) (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
- trust in automated vehicles, risk perception, relational risk, automation reliability, driver-automation interaction, trust calibration, driving scenarios, traffic types
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