(Don’t) Talk to Me! Application of the Kano Method for Speech Outputs in Conditionally Automated Driving
DOI: 10.54941/ahfe1002484
archive: archived pipeline: cataloged verified
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Summary
This study investigates user preferences for speech outputs in human-machine interfaces (HMI) for conditionally automated driving (Level 3). Level 3 automation requires repeated transitions of driving responsibility between the automated system and the human operator, making effective communication critical. While speech outputs offer gaze-free information delivery and reduced visual workload, they risk causing annoyance if overused. The research aims to determine which specific scenarios warrant speech outputs and how users prefer to be addressed, addressing a gap in literature regarding differentiated user preferences across various HMI functions. The methodology employed the Kano method to categorize user satisfaction with six potential speech output features: availability changes (up/down), transitions between automation levels, requests to intervene, hands-off warnings, and operating error feedback. The study involved 42 drivers who completed a 45-minute test drive in a modified BMW 3 Series on a test track, experiencing various automation levels and transition scenarios. Following the drive, participants completed a survey using functional and dysfunctional Kano questions to evaluate each feature. Additionally, participants indicated their preference for being addressed in a passively distanced versus actively personal manner and provided open-ended comments. Results from discrete and continuous Kano analyses revealed distinct user attitudes based on scenario criticality. Speech outputs for "Availability Change-Down" (e.g., sensor errors) and "Request to Intervene" were classified as Performance features, indicating that user satisfaction increases proportionally with the presence of these outputs. These scenarios involve critical information requiring immediate attention. Conversely, "Operating Error" feedback was categorized as Indifferent, suggesting users do not strongly prefer or dislike speech for this function. Features such as "Availability Change-Up" and "Transition" showed mixed results, with significant variance among participants. Regarding address style, preferences were highly divided; over half of participants preferred a passively distanced tone, while others favored personal addressing. Open comments highlighted a strong desire for customization, including the ability to mute speech or adjust volume, and noted that speech is most valued in critical situations. The findings suggest that speech outputs are most beneficial for critical scenarios, such as system failures or take-over requests, where they enhance safety and clarity. In non-critical contexts, user attitudes vary significantly, implying that a one-size-fits-all approach is ineffective. The study concludes that HMI design for Level 3 driving should prioritize customizable speech outputs, allowing users to tailor volume, voice, and addressing style to their preferences. Future research should focus on longitudinal studies to understand how preferences evolve with experience and to identify specific user groups that may benefit from different HMI configurations.
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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 | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified_with_issues.
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