User Experience and Behavioural Adaptation Based on Repeated Usage of Vehicle Automation: Online Survey
DOI: 10.14569/ijacsa.2024.0150304
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Summary
This study investigates user experience (UX), trust, and behavioral adaptation regarding Level 2 vehicle automation systems (VAS) during repeated usage in urban traffic. The research is motivated by the commercial availability of Level 2 automation and the persistent uncertainty surrounding users' comprehension of these systems' capabilities and limitations. The authors aim to understand how prolonged exposure to automation influences user knowledge, learning patterns, and acceptance, specifically focusing on the transition from manual to automated driving tasks. The study seeks to provide user-centric insights to inform interaction design strategies and policies that enhance the safety and usability of automated vehicles. The researchers employed an online survey methodology to collect data from 16 drivers with experience in automated driving. The survey instrument gathered demographic information and assessed specific knowledge domains, including automated driving experience timeframes, vehicle operation competency, driving skills over long-term use, the learning process, automation-induced effects, trust in automation, and perceptions of ADS researchers and manufacturers. The study conceptualizes behavioral adaptation through the lens of "Learnability in Automated Driving" (LiAD), distinguishing between users who learn to misuse automation versus those who learn to use it responsibly. The analysis considers various factors influencing behavioral change, such as system design, environmental conditions, and user states, while examining synergies of effects related to trust, situational awareness, and skills. The findings reveal that users' knowledge of automation directly correlates with their learning patterns, trust levels, and acceptance of the technology. The study identifies that attitudes toward trust and acceptance vary significantly across different user profiles. Furthermore, the research highlights that automated driving alters the safety and risk conditions of user-vehicle interactions. The results indicate that long-term exposure leads to behavioral modifications, where users adapt to changing driving situations and system interfaces. These adaptations are influenced by the interplay of physical road infrastructure changes, social contexts, and the specific design of human-machine interfaces. The study underscores that behavioral evolution is not uniform but depends on individual user peculiarities and the specific functionalities of the automation systems, such as longitudinal and lateral driver support. The significance of this work lies in its contribution to understanding the long-term dynamics of human-automation interaction. By linking user knowledge to behavioral adaptation and trust, the study provides a foundation for developing resilient interaction design strategies. The findings suggest that effective UX design must account for the learning phases of users, particularly the "learning and appropriation" phase, to foster responsible use and prevent misuse. The research implies that future automated vehicle systems should prioritize clear communication of system capabilities and limitations to support the formation of accurate mental models. Ultimately, these insights are critical for ensuring the safe adoption of automated vehicles and for shaping regulatory frameworks and industry standards that align with human factors and quality-based interaction design.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-07 |
| archive | success | canonical_url | — | — | 1 | 2026-06-09 |
| extract | success | pdftotext | — | — | 2 | 2026-06-09 |
| clean | success | clean | — | — | 1 | 2026-06-09 |
| chunk | success | chunk | — | — | 1 | 2026-06-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-09 |
| enrich | success | openalex | — | — | 3 | 2026-07-02 |
| promote | success | — | — | — | 1 | 2026-06-07 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-09 |
| tag | success | vector_similarity | — | — | 8 | 2026-06-11 |
| verify | partial | — | — | — | 1 | 2026-06-09 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-09; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- trust calibration
- automation surprise
- acceptance adoption
- automation
- automation complacency bias
- mode awareness
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: self report data, observational prevalence
- Theoretical Contribution: conceptual framework