Audiovisual presentation of variable message signs improves message processing in distracted drivers during partially automated driving
DOI: 10.1016/j.jsr.2024.01.014
archive: archived pipeline: cataloged verified
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Summary
This study investigates whether adding auditory components to visual variable message signs (VMS) improves message processing for drivers engaged in distracting secondary tasks during partially automated driving. While highly automated vehicles reduce accident risks from human error, they also increase driver distraction, as occupants may engage in non-driving-related tasks. Previous research indicated that auditory signals aid in takeover scenarios, but it remained unclear if verbal auditory messages benefit drivers who are simultaneously visually and auditorily distracted. The authors aimed to compare the effectiveness of audiovisual VMS messages against visual-only messages and to assess eye-tracking data to understand visual resource allocation. The experiment utilized a driving simulator with 24 adult participants who completed two concurrent tasks: watching a TV series (an audiovisual distractor) and monitoring VMS for critical traffic information requiring manual takeover. The study employed a within-subject design comparing visual-only versus audio-plus-visual conditions, with counterbalanced order. Participants drove a simulated motorway route while viewing the TV series on an iPad. VMS displayed critical or informative messages; critical messages required the driver to press a lever to regain control. Eye-tracking glasses recorded glance behavior, including latency, duration, and frequency of road glances. Signal Detection Theory metrics were used to measure sensitivity and response bias. Results demonstrated that the audiovisual condition significantly improved message processing compared to the visual-only condition. Participants in the audio-plus-visual condition exhibited higher sensitivity in discriminating between critical and non-critical messages, responded earlier, and were less conservative in their response bias. Eye-tracking data revealed a more efficient visual pattern in the audiovisual condition, characterized by shorter fixation durations and less total time glancing at the road. This suggests that auditory information reduced the visual load required to process the VMS content. A complementary analysis indicated that the order of conditions acted as a moderating factor, suggesting a potential learning effect that was not fully eliminated by counterbalancing. Participants maintained high engagement with the TV series, scoring an average of 9.5 out of 10 on a comprehension test, confirming the effectiveness of the distraction task. The findings conclude that providing traffic messages as both oral and visual inputs enhances processing efficiency and safety for distracted drivers in automated vehicles. By reducing visual demand and improving discrimination accuracy, audiovisual VMS can help ensure drivers maintain sufficient awareness to take over control when necessary. These results have practical implications for engineers designing automated systems, highlighting the importance of multimodal information presentation to mitigate the risks associated with driver distraction in SAE Level 2 and 3 automation contexts.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | openalex | — | — | 5 | 2026-08-09 |
| extract | success | cached | — | — | 3 | 2026-08-10 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| enrich | success | semantic_scholar | — | — | 1 | 2026-08-09 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 10 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
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- Applied Guidance: design guidelines
- Empirical Findings: behavioral performance data
- Theoretical Contribution: conceptual framework