Anthropomorphizing information to enhance trust in autonomous vehicles
DOI: 10.1002/hfm.20745
archive: archived pipeline: cataloged verified
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Summary
This study investigates whether anthropomorphizing information displays can enhance user trust in autonomous vehicles (AVs). While AV technology is advancing, public acceptance remains hindered by distrust and a lack of transparency regarding vehicle actions. The authors posit that providing information about the vehicle’s intended maneuvers can improve system transparency, and that adding anthropomorphic characteristics to this information may further foster trust by encouraging users to perceive the vehicle as a social agent. The researchers conducted a driving simulator experiment with 39 participants using a between-subjects design. Participants were divided into two groups: one received symbolic information (icons indicating upcoming actions like turning or braking), and the other received symbolic information supplemented with anthropomorphic visualizations (animated eyes that changed color or direction based on vehicle actions). Both groups experienced a baseline condition with no information first, followed by their assigned experimental condition. This design allowed for comparisons between the two information types and against the no-information baseline. Participants rated their trust, liking, and perceived anthropomorphism of the vehicle relative to the baseline using questionnaires, and provided qualitative feedback through interviews. The results demonstrated that the symbolic + anthropomorphic condition significantly increased perceived anthropomorphism and trust compared to the symbolic-only condition. One-sample t-tests revealed that while the symbolic-only condition did not significantly differ from the no-information baseline in terms of trust or liking, the symbolic + anthropomorphic condition resulted in significantly higher ratings for trust, liking, and perceived anthropomorphism compared to the baseline. Additionally, perceived anthropomorphism was positively correlated with both trust and liking. Qualitative data indicated that while most participants found the information helpful, some in the anthropomorphic group felt uncomfortable with the eye animations, particularly when the eyes looked down during deceleration, which they interpreted as inattention. The study concludes that anthropomorphizing information about an autonomous vehicle’s maneuvers can effectively enhance user trust by fostering the perception of the vehicle as a social agent. The findings suggest that combining symbolic transparency with anthropomorphic cues is more effective for building trust than symbolic information alone. However, the design of anthropomorphic features requires careful consideration to avoid negative reactions, such as the "uncanny valley" effect or misinterpretations of the vehicle's attention. These insights contribute to the field of human-vehicle interaction by highlighting the potential of anthropomorphism to bridge the trust gap in autonomous driving technologies.
Key finding
Providing symbolic information combined with anthropomorphic visualizations significantly increased trust in autonomous vehicles compared to symbolic information alone and a no-information baseline.
Methodology
simulator
Sample size: 39
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-05 |
| archive | success | canonical_url | — | — | 1 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| promote | success | — | — | — | 1 | 2026-06-05 |
| 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.
Topics
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- trust in automation foundations
- trust calibration
- automation
- acceptance adoption
- ehmi external hmi
- automation surprise
Information type
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- Empirical Findings: self report data