Dynamic scan paths investigations under manual and highly automated driving
DOI: 10.1038/s41598-021-83336-4
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Summary
This study investigates the sequential organization of visual scanning, or scan paths, during driving to understand how gaze strategies differ between manual driving (MD) and highly automated driving (HAD). While previous research has established the coupling of gaze and steering, the specific sequences of fixations that drivers use to sample visual information remain poorly understood, particularly in dynamic environments. The authors aim to identify stereotypical visual sequences and determine how automation alters these patterns, addressing the challenge that traditional static Areas of Interest (AOIs) are insufficient for analyzing dynamic driving scenes. The experiment utilized a fixed-base driving simulator with a 145° field of view. Sixteen licensed drivers performed a car-following task on a winding rural road, first under manual control and then under HAD, where the system controlled lateral and longitudinal movements while participants monitored the environment. The HAD condition was a replay of the participant’s own MD performance, ensuring identical visual stimuli. Eye movements were recorded at 50 Hz and classified into seven dynamic AOIs, including the leading vehicle, near and far road sections, and off-road areas. The researchers analyzed scan paths—sequences of consecutive AOI visits—selecting a length of four fixations as the optimal balance between sequence complexity and data manageability. The analysis revealed five stereotypical scan paths under manual driving: forward polling (far road exploration), guidance, backwards polling (near road exploration), scenery, and speed monitoring. Notably, backwards polling was the most frequent pattern, a finding previously undocumented in the literature. Under HAD, the visual strategy shifted significantly: the relative frequency of backwards polling decreased, while guidance scan paths increased. Additionally, new scan paths specific to automation supervision emerged. The diversity of scan paths was higher in HAD, with 634 distinct types identified compared to 351 in MD. However, a small subset of frequent scan paths accounted for the majority of observations in both conditions. Statistically, HAD reduced fixations on the near road (AOI 2) and showed a trend toward increased look-ahead fixations (AOI 4), indicating a shift from immediate control monitoring to broader environmental surveillance. These findings demonstrate that driving automation fundamentally restructures visual exploration strategies. The reduction in near-road polling and the emergence of supervision-specific patterns suggest that drivers in HAD modes disengage from immediate visuo-motor control loops in favor of monitoring the automation system and the broader environment. This study provides a methodological framework for analyzing dynamic scan paths and offers empirical evidence that automation changes not just where drivers look, but the sequential logic of their visual sampling, with implications for designing safer human-machine interfaces in automated vehicles.
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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
Ranked by relevance to this paper. Hover a topic for its definition.
- eye movements scanning
- attention allocation
- gaze based attention detection
- peripheral attention
- temporal
- useful field of view
Information type
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- Empirical Findings: behavioral performance data
- Methodological Resource: measurement protocol, tool software