How the Initial Level of Trust in Automated Driving Impacts Drivers’ Behaviour and Early Trust Construction
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Summary
This study investigates how a driver’s initial level of trust in automated driving (TiAD) influences their behavior and the subsequent construction of trust during Highly Automated Driving (HAD). Motivated by the need to understand trust calibration processes to ensure safe human-automation interaction, the research examines whether drivers with high initial trust ("Trustful") and low initial trust ("Distrustful") exhibit different visual strategies and engagement in Non-Driving-Related Activities (NDRA) during early exposure to HAD. The experiment involved 40 licensed drivers selected from a larger pool based on pre-assessed trust levels, divided into Trustful (n=20) and Distrustful (n=20) groups. Participants completed a 30-minute driving simulator session on a highway scenario, engaging in HAD while free to perform NDRA. The session included two critical scenarios—a roadwork zone and a slow-moving truck—designed to test system boundaries without triggering takeover requests. Data collected included self-reported trust via nine-item questionnaires and single-item scales, as well as eye-tracking metrics for gaze behavior and video-coded NDRA engagement. Results indicated that initial trust levels significantly impacted driver behavior. Trustful drivers engaged more frequently in NDRA and spent less time monitoring the driving environment compared to Distrustful drivers, who maintained higher vigilance. Despite these behavioral differences, declared trust increased for both groups over the course of the experiment. However, the increase was more pronounced for Distrustful drivers, suggesting a greater trust gain for those initially skeptical. Nevertheless, significant differences between the groups persisted at the end of the session, with Trustful drivers maintaining higher overall trust. Additionally, the order of critical scenarios affected trust evolution; participants experienced different trust trajectories depending on whether they encountered the roadwork or truck scenario first. Visual analysis confirmed that drivers reduced their monitoring of the driving environment over time, particularly during the first 10 minutes, with Distrustful drivers consistently allocating more visual attention to the road than their Trustful counterparts. The findings suggest that initial TiAD is a stable predictor of driver behavior and trust calibration during early HAD use. While experience with the system increases trust for all users, individual differences in initial trust lead to distinct monitoring strategies and activity engagement. This implies that HAD systems must account for varying initial trust levels to ensure appropriate safety margins, as Distrustful drivers may remain overly vigilant while Trustful drivers may disengage too quickly. Understanding these dynamics is crucial for designing interfaces and automation behaviors that foster well-calibrated trust, thereby enhancing both safety and user acceptance in automated vehicles.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | semantic_scholar | — | — | 6 | 2026-08-09 |
| extract | success | pdftotext | — | — | 4 | 2026-08-10 |
| clean | success | clean | — | — | 2 | 2026-08-10 |
| chunk | success | chunk | — | — | 2 | 2026-08-10 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 2 | 2026-08-10 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 17 | 2026-08-11 |
| verify | partial | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- trust calibration
- trust in automation foundations
- automation
- automation surprise
- acceptance adoption
- situational awareness
Information type
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- Empirical Findings: self report data
- Theoretical Contribution: computational model, conceptual framework