Different feedback strategies: Evaluation of active vehicle motions in a multi-level system
DOI: 10.54941/ahfe1002468
archive: archived pipeline: cataloged verified
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Summary
This study addresses the challenge of maintaining driver mode awareness in multi-level automated driving systems, where responsibilities shift between partial (Level 2) and high (Level 4) automation. The coexistence of these modes risks mode errors, such as inappropriate driver disengagement or delayed responses to take-over requests. While prior research has focused on visual and auditory feedback, this work investigates the efficacy of vestibular feedback—specifically active vehicle pitch and roll motions—as a complementary modality. The primary research question examines whether adding vestibular cues during partially automated driving improves system comprehension, trust, and acceptance compared to a visual-auditory only strategy. The researchers conducted a real-world driving study on the German highway A9 with 36 participants. Drivers were randomly assigned to one of two feedback strategies: visual-auditory (VA) or visual-auditory-vestibular (VAV). Both strategies provided identical visual information in the instrument cluster and auditory signals. The VAV strategy additionally utilized pitch motions to indicate detected slower preceding vehicles and roll motions to announce lane changes during Level 2 driving. Participants experienced transitions between manual, partially automated, and highly automated driving. Data on mode awareness, trust, and acceptance were collected via questionnaires after the test drive and following a simulated system failure. Statistical analyses included t-tests, Mann-Whitney U tests, and mixed ANOVAs. The results indicated that the VAV strategy significantly enhanced system comprehension and trust compared to the VA strategy. Specifically, participants in the VAV group reported higher reliability and greater trust in automation. However, no significant differences were found between the groups regarding other aspects of mode awareness, such as monitoring behavior or task awareness. Task awareness was significantly lower during partially automated driving than during manual or highly automated driving, suggesting that shared responsibility complicates task comprehension. Regarding acceptance, both strategies were rated as useful and satisfying. A simulated system failure decreased perceived reliability for all participants but did not reveal significant differences between feedback strategies, likely due to the smaller sample size (N=25) for that segment. The study concludes that incorporating vestibular feedback into multimodal Human-Machine Interfaces can increase the perceived reliability and trust in automated vehicles, particularly during partially automated driving. These findings support the use of active vehicle motions to distinguish automation levels and improve driver understanding of system states. The results imply that differentiating feedback designs for different levels of automation is beneficial for user acceptance and safety. Limitations include potential self-selection bias and the difficulty of standardizing real-world traffic conditions, suggesting future research should explore long-term effects and include less interested user groups.
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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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- Applied Guidance: countermeasure evaluation
- Empirical Findings: self report data
- Theoretical Contribution: conceptual framework