Adaptations of drivers’ gaze behaviour during car-following as a function of vehicle automation, duration of use and prior driving experience
DOI: 10.1007/s10111-026-00861-w
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Summary
This study investigates how vehicle automation, duration of use, and prior driving experience influence drivers’ gaze behavior during car-following tasks. As automation shifts the driver’s role from active operator to passive supervisor, understanding behavioral adaptations is critical for safety and system design. The research specifically addresses gaps in existing literature regarding the impact of prolonged automation exposure and the behavior of completely inexperienced drivers, who may represent future users of autonomous vehicles. The experiment utilized a fixed-based driving simulator with eye-tracking technology to record gaze positions at 50 Hz. Sixty-four participants, divided equally into experienced drivers (licensed for at least four years) and novices (never licensed), were assigned to one of four automation conditions: Non-Automated (NA, SAE Level 0), Automated Steering (AS, SAE Level 1), Highly Automated Driving (HAD, SAE Level 3), and Fully Automated Driving (FA, SAE Level 5). Participants engaged in a car-following task over seven consecutive weeks. Data analysis focused on dynamic Areas of Interest (AOIs)—including the speedometer, lead vehicle, near road, future road, and scenery—and categorized gaze sequences into five scan path types: forward polling, guidance, backward polling, scenery, and speed monitoring. Statistical analyses included mixed-design ANOVAs to assess effects of automation, experience, and time. Results indicate that automation significantly alters gaze distribution compared to manual driving. Automation promotes scenery and supervision scan paths while reducing driving-control scan paths, such as those monitoring the road and vehicle speed. This shift became pronounced when drivers disengaged from physical control (HAD and FA conditions). Specifically, drivers in HAD and FA conditions spent significantly less time looking at the near road and speedometer but more time monitoring the lead vehicle compared to NA and AS drivers. Driving experience also influenced gaze behavior; novice drivers focused more on speed-monitoring scan paths, whereas experienced drivers exhibited more balanced scan paths. Notably, no significant changes in scan paths were observed over the seven-week period, suggesting that gaze strategies are highly context-dependent rather than adaptive over time. The findings provide critical insights into the dynamics of drivers’ gaze behavior under varying automation levels. The study demonstrates that the transition from active control to supervision fundamentally changes visual sampling strategies, with implications for designing driver monitoring systems. By identifying specific scan path deviations, such as increased supervision gazes in highly automated conditions, the research supports the development of more nuanced safety interventions beyond traditional "eyes-off-road" metrics. Additionally, the lack of adaptation over time highlights the need for training or system designs that account for stable, context-dependent gaze behaviors in automated driving scenarios.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-05 |
| archive | success | canonical_url | — | — | 1 | 2026-06-09 |
| extract | success | cached | — | — | 2 | 2026-06-09 |
| clean | success | clean | — | — | 1 | 2026-06-05 |
| chunk | success | chunk | — | — | 1 | 2026-06-05 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-05 |
| promote | success | — | — | — | 1 | 2026-06-05 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-09 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-09 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-09; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- eye movements scanning
- attention allocation
- temporal
- trust calibration
- situational awareness
- automation surprise
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: behavioral performance data
- Methodological Resource: tool software
- Theoretical Contribution: theory or model