Experiencing Automated Vehicles in Real-Life Affects Central Aspects of Drivers’ User Experience
DOI: 10.1007/978-3-032-03488-5_4
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Summary
This study investigates how real-life experience with automated vehicles influences drivers’ user experience evaluations and their intention to use the technology. While prior research often relied on simulators or anticipated expectations, this work addresses the gap in understanding how actual on-road interaction alters perceptions of instrumental qualities (usability, usefulness) and non-instrumental qualities (visual aesthetics, status, emotions). The authors posit that positive or negative expectations formed before interaction significantly impact adoption, and they examine whether these evaluations shift after a real-world driving experience. The researchers conducted a field study with 38 licensed drivers who completed a one-hour, 24-kilometer drive in a 2016 Tesla Model S equipped with Autopilot 2.0 (Level 2 automation). The route included rural roads and highways. Participants completed the meCUE questionnaire to assess their expectations before the drive and their experiences afterward. The questionnaire measured instrumental qualities (usefulness, usability), non-instrumental qualities (visual aesthetics, status, commitment), emotions (positive and negative), and behavioral consequences (intention to use, product loyalty, overall evaluation) using Likert-type scales. Drivers were free to engage the automation at their discretion but remained responsible for safety. Results from paired t-tests revealed that real-life experience significantly increased drivers’ ratings of instrumental qualities, specifically usefulness and usability, as well as their overall evaluation and intention to use the system. Conversely, there were no significant changes in non-instrumental qualities (visual aesthetics, status, commitment) or emotional responses (positive and negative emotions), suggesting that hedonic aspects remained stable despite the driving experience. Linear regression analyses further demonstrated that pre-experience ratings of usefulness, status, and positive emotions significantly predicted the intention to use the vehicle. These same three factors remained significant predictors of intention to use even after the driving experience, accounting for approximately 25% of the variance in both pre- and post-experience models. Notably, usability and negative emotions did not significantly predict intention to use, despite occasional automation failures and usability issues reported by participants. The findings indicate that while real-world experience enhances perceived instrumental value, the core drivers of adoption intention—usefulness, status, and positive emotions—remain consistent from anticipation to experience. This stability contrasts with some simulator-based studies where trust or safety perceptions shifted more dramatically. The study highlights the importance of designing automated vehicles that maintain high visual aesthetics and positive emotional engagement, as these factors, alongside perceived usefulness, are critical for user acceptance. The authors conclude that future research should explore longer-term effects and naturalistic settings to better understand the evolution of user experience and its implications for traffic safety and technology adoption.
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 |
| 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 | success | — | — | — | 1 | 2026-06-09 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-09; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- acceptance adoption
- trust calibration
- automation
- automation surprise
- situational awareness
- passenger motion sickness comfort
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
- Methodological Resource: validation psychometrics
- Theoretical Contribution: computational model