The Effect of Driving Automation on Drivers' Anticipatory Glances
DOI: 10.1007/978-3-030-74608-7_80
archive: archived pipeline: cataloged verified
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Summary
This paper presents a secondary analysis of data from two driving simulator experiments to investigate how SAE Level 2 driving automation affects drivers’ visual attention toward anticipatory cues for upcoming traffic events. The research addresses a gap in literature regarding how automation influences driver anticipation, specifically examining whether relieving drivers of physical control allows for better monitoring of environmental cues that signal potential conflicts. The study compares glance behaviors between non-automated driving and driving with combined adaptive cruise control (ACC) and lane keeping assistance (LKA), while also assessing the moderating effects of driver experience and engagement in a secondary task. The methodology involved 64 participants (32 per experiment), split evenly between novice and experienced drivers. Each experiment utilized a 2x2 factorial design varying driving experience and the availability of a self-paced visual-manual secondary task simulating infotainment system use. Participants completed four drives in a fixed-base simulator, each containing scenarios with specific anticipatory cues preceding traffic events. Eye-tracking data recorded glances toward the roadway and specific anticipatory cues. Statistical analysis employed mixed models to evaluate the percentage of time spent looking at cues and the roadway, as well as the time until the first glance at cues after their onset. The results revealed a significant interaction between automation and secondary task engagement. In the absence of a secondary task, drivers using automation spent 10% more time looking at anticipatory cues than those driving without automation, suggesting that automation frees up attentional capacity for monitoring the road. However, when a secondary task was available, this benefit disappeared; drivers with automation did not differ from those without in their attention to cues. Furthermore, automation combined with a secondary task led to a substantial reduction in roadway monitoring. Drivers with automation spent 46% less time looking at the roadway when distracted, compared to a 27% reduction for non-automated drivers. This indicates that automation encourages drivers to shift attention away from the driving task when distracted. The study concludes that while driving automation can enhance visual attention to anticipatory cues when drivers are not distracted, this benefit is negated by secondary task engagement. Distraction counteracts the potential advantages of automation, leading to reduced monitoring of both the roadway and specific cues. The authors imply that interventions, such as improved display design or training, are necessary to mitigate distraction and maintain driver anticipation capabilities in automated vehicles. Future research is recommended to explore scenarios requiring driver intervention to avoid collisions, as the current study focused on events navigable by automation.
Key finding
Driving automation increases visual attention to anticipatory traffic cues only when drivers are not engaged in a secondary task, as distraction eliminates this benefit and reduces overall roadway monitoring.
Methodology
simulator
Sample size: 64
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-28 |
| archive | success | canonical_url | — | — | 1 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| enrich | failed | — | — | — | 5 | 2026-07-02 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Empirical Findings: behavioral performance data
- Methodological Resource: tool software, measurement protocol