Improving Passenger Experience and Trust in Automated Vehicles Through User-Adaptive HMIs: “The More the Better” Does Not Apply to Everyone
DOI: 10.3389/fhumd.2021.669030
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Summary
This study addresses the challenge of fostering trust and acceptance in higher-level automated vehicles (SAE Levels 4–5), where the shift from active driving to passive passenger status often causes user reservations. The authors investigate whether user-adaptive Human-Machine Interfaces (HMIs) can improve passenger experience and trust by tailoring information availability to individual user characteristics, specifically initial trust levels. The research challenges the assumption that providing maximum information ("the more the better") is universally beneficial, proposing instead that HMI design should adapt to varying user needs. The researchers conducted a driving simulator study with 50 first-time users of automated driving, divided into lower-trust and higher-trust groups based on a median split of initial trust scores. Participants experienced three identical automated rides under different HMI conditions: a baseline with no automation-specific information, a "permanent" HMI displaying full system status and traffic details continuously, and a "context-adaptive" HMI that displayed reduced information during simple driving but switched to full information during complex situations (e.g., intersections). The study measured perceived safety, understanding of driving behavior, comfort, enjoyment, and trust in the automation. Results indicated that presenting driving-related information via an HMI improved all assessed aspects of passenger experience and trust compared to the no-HMI baseline. However, the effectiveness of specific HMI designs depended on the user’s initial trust. The higher-trust group reported the highest levels of safety, understanding, and comfort with the context-adaptive HMI. Conversely, the lower-trust group tended to experience the highest safety, understanding, and comfort with the permanent HMI. While both HMIs received positive user experience ratings, the context-adaptive version was generally preferred, particularly by the higher-trust group. The findings demonstrate that increasing system transparency through HMIs enhances trust and passenger experience, but a one-size-fits-all approach is ineffective. The study concludes that user-adaptive HMI concepts, which customize information availability based on individual characteristics like initial trust, are necessary to optimize the passenger experience for diverse user groups. Providing permanent full information is not recommended as a universal standard, as it may not meet the specific needs of all users. These insights support the development of personalized HMI designs to facilitate the market acceptance and societal benefits of automated vehicles.
Key finding
While HMI information generally improved passenger experience and trust, users with higher initial trust preferred context-adaptive information displays, whereas users with lower initial trust preferred permanent information displays.
Methodology
simulator
Sample size: 50
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
Ranked by relevance to this paper. Hover a topic for its definition.
- automation
- trust calibration
- acceptance adoption
- automation surprise
- ehmi external hmi
- trust in automation foundations
Information type
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- Applied Guidance: design guidelines
- Empirical Findings: self report data