An online-survey on user expectations and mental model of automated driving: The effects of automation description and technology readiness
DOI: 10.54941/ahfe1005214
archive: archived pipeline: cataloged verified
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Summary
This study investigates user mental models and expectations regarding Level 3 Automated Driving (AD), specifically examining how different automation descriptions and individual technology readiness influence these perceptions. Motivated by the European Hi-Drive project’s goal to enhance AD safety and usability, the research addresses the critical gap in understanding how drivers comprehend system capabilities and limitations. Previous literature indicates that incorrect mental models of Advanced Driver Assistance Systems (ADAS) lead to automation expectation mismatches and safety risks. Consequently, this study aims to assess whether technical (SAE) versus user-oriented (BASt) descriptions of AD improve user understanding and how technology readiness—a predisposition to embrace new technologies—shapes these mental models. The researchers conducted an online survey with 211 licensed drivers, recruited from a test-driver panel. Participants were randomly assigned to read either the SAE or BASt definition of Level 3 AD. They then completed a 28-item questionnaire assessing their mental model of AD system characteristics, their confidence in their answers, and their expectations of specific AD capabilities (e.g., handling adverse weather, complex infrastructure). Participants were categorized into low, medium, and high technology readiness groups based on a standardized scale. The analysis compared the proportion of correct answers, confidence levels, and feature expectations across these groups and instruction types. Results indicated no significant difference in mental model accuracy between the SAE and BASt instruction groups, suggesting that neither description alone was sufficient to convey correct handling procedures. However, technology readiness significantly impacted understanding. Participants with high technology readiness, who were typically younger, male, and had more experience with ADAS, demonstrated a better mental model of AD, particularly regarding system activation, deactivation, and availability. These users also reported higher confidence in their answers and evaluated AD more positively in terms of safety, comfort, and trust. Regarding expectations, the majority of users anticipated features like stop-and-go traffic handling and automated lane changes. However, expectations were ambiguous for complex infrastructure, and most did not expect AD to operate in adverse weather or at high speeds. High technology readiness users were more likely to expect features like speed limit offsets and operation in darkness. The study concludes that user education for AD must go beyond static textual descriptions, as both SAE and BASt definitions failed to adequately communicate system handling. Instead, interactive tutorials or videos may be more effective. Furthermore, AD development and user training should account for technology readiness, as experienced users derive expectations from their prior ADAS usage. Developers should prioritize features aligned with user expectations, such as early takeover requests, while clearly communicating system boundaries for complex scenarios to prevent expectation mismatches.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 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 | — | — | — | 1 | 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.
- acceptance adoption
- situational awareness
- automation surprise
- trust calibration
- automation
- mental model of traffic
Information type
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- Empirical Findings: self report data
- Theoretical Contribution: conceptual framework, computational model