Anticipated emotions associated with trust in autonomous vehicles
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Summary
This study investigates the relationship between anticipated emotions and trust in autonomous vehicles (AVs), addressing a gap in research that has predominantly focused on cognitive perspectives of trust. While prior work acknowledges that emotions influence trust judgments, the specific structure of anticipated emotions associated with varying levels of AV trust remains unclear. The authors aimed to determine how low versus high levels of trust, elicited by different AV performance scenarios, affect the pattern of 19 discrete anticipated emotions. Understanding these relationships is critical for developing trust calibration strategies that prevent over-trust or under-trust in human-machine interactions. The researchers conducted an online survey with 105 participants recruited from Amazon Mechanical Turk. The experimental design manipulated trust levels using two conditions: a "low trust" condition featuring negative information and a video of an AV failing to avoid a crash, and a "high trust" condition featuring positive information and a video of an AV successfully handling a critical situation. Trust was assessed across three layers: dispositional, initial learned, and situational. Participants rated their anticipated emotions using a 19-item scale covering positive, negative, and mixed affective states. Data analysis included one-way ANOVAs to compare emotion ratings between conditions and exploratory factor analysis (EFA) to identify the underlying latent structures of emotions before and after the situational trust manipulation. The results indicated that AV performance significantly influenced situational trust but had no significant effect on dispositional or initial learned trust. Regarding emotions, there were no significant differences between conditions before the video manipulation. However, after viewing the videos, participants in the high trust condition reported significantly higher positive emotions (e.g., confident, secure, grateful) and significantly lower negative emotions (e.g., disdainful, hostile, afraid) compared to those in the low trust condition. Factor analysis revealed distinct emotional structures associated with each trust level. In the low trust condition, negative emotions clustered into factors such as "Resentfully Aversion" and "Nervously Fear." In the high trust condition, emotions structured around "Happily Acceptance" and "Resentfully Aversion," though the latter contained fewer items. The findings demonstrate that high situational trust enhances positive emotional responses while suppressing negative ones. The study concludes that situational trust is a key driver of anticipated emotional states in AV interactions. The identified emotional structures suggest that trust calibration interventions should consider affective responses alongside cognitive assessments. By understanding how specific emotions cluster under different trust conditions, designers can better tailor system feedback to promote appropriate trust levels. The authors note limitations regarding the low-fidelity video stimuli and reliance on self-reported measures, suggesting future research should utilize driving simulators and physiological data to validate these findings. Ultimately, the work provides a foundation for using emotion as a heuristic to improve trust calibration in automated driving systems.
Key finding
Higher levels of situational trust in autonomous vehicles were significantly associated with increased positive emotions and decreased negative emotions, whereas lower trust levels produced the opposite emotional pattern.
Methodology
survey
Sample size: 105
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | canonical_url | — | — | 1 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Empirical Findings: self report data