Multimodal interface and reliability displays: Effect on attention, mode awareness, and trust in partially automated vehicles
DOI: 10.3389/fpsyg.2023.1107847
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Summary
This study investigates how multimodal interfaces and reliability displays affect driver attention, mode awareness, and trust in partially automated vehicles (PDA). Partial driving automation, such as Lane Centering Assist combined with Adaptive Cruise Control, requires drivers to monitor the system and take over when automation limits are reached. The research addresses critical challenges in human-machine interaction, including the "out-of-the-loop" phenomenon, mode confusion, and miscalibrated trust. The authors hypothesized that a Multimodal Interface with an Indicator of Limits of Automation (MILA) would outperform a standard Visual Basic Interface (VBI) by stimulating appropriate attention, fostering accurate mental models, and inducing calibrated trust through prolonged exposure. The experiment utilized a high-fidelity driving simulator with 40 participants aged 39–65. Participants were randomly assigned to either the VBI condition, which provided only visual status updates, or the MILA condition. The MILA included an Indicator of Proximity to the Limits of Automation (IPLA) displayed in peripheral vision, auditory earcons signaling control transitions, and haptic feedback in the steering wheel indicating system state and suspensions. Over a three-week period, participants completed three driving sessions involving scenarios that triggered automation suspensions, such as sharp bends, erased road markings, traffic jams, and fog. The study measured driving performance, ocular behavior, mental models, trust, and workload, comparing pre-test and post-test results to assess learning effects. The results indicated that the MILA successfully stimulated an appropriate level of attention and increased both mode awareness and trust in the automation system compared to the VBI. The multimodal cues helped drivers anticipate automation limits and understand the system's functioning more accurately. However, these benefits were context-dependent. Crucially, while the MILA improved drivers' knowledge regarding the automation's limits and functioning, this enhanced understanding did not necessarily translate into improved driving performance during take-over maneuvers. The study found that better mental models and trust did not automatically result in safer or more efficient vehicle control when automation suspended. The findings suggest that while multimodal interfaces and reliability displays are effective for enhancing driver awareness and trust, they are insufficient on their own to guarantee safe take-overs. The disconnect between improved mental models and actual driving performance highlights the need for additional design solutions to support driver intervention. The study concludes that interface design must go beyond information provision to actively facilitate the transition from monitoring to controlling the vehicle, ensuring that increased awareness leads to actionable and safe driving behaviors in partially automated systems.
Provenance
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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.
Topics
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- automation
- mode awareness
- trust calibration
- situational awareness
- automation surprise
- automation complacency bias
Information type
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- Empirical Findings: self report data
- Theoretical Contribution: conceptual framework, theory or model