Situational Awareness in the Context of Automated Driving - Adapting the Situational Awareness Global Assessment Technique
DOI: 10.54941/ahfe1003802
archive: archived pipeline: cataloged verified
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Summary
This study addresses the challenge of measuring situational awareness (SA) in SAE Level 3 automated driving, specifically during Take-Over Requests (TORs). When drivers disengage from driving tasks, they must rapidly regain control upon a TOR, often in complex scenarios like accidents or roadworks. The Situation Awareness Global Assessment Technique (SAGAT) is a standard method for measuring SA, but it traditionally treats all environmental information as equally relevant. This paper aims to adapt SAGAT by weighting questions based on their relevance to safe vehicle takeover, hypothesizing that a weighted evaluation would provide a more accurate measure of SA and correlate better with takeover performance. The methodology involved two phases. First, a literature review identified potential SAGAT questions for "Accident" and "Roadworks" scenarios, which were then categorized into five types: own vehicle/behavior, surrounding vehicles, traffic rules, navigation, and TOR cause. An online survey with 78 participants rated the relevance of these items, allowing the researchers to classify them into high, medium, and low weighting categories. Second, a driving simulator study with 32 participants tested this weighted approach. Participants experienced autonomous driving followed by a TOR in both scenarios. The simulation was paused unannounced to administer SAGAT questions, and takeover performance was recorded using metrics such as time to intervention, collisions, and steering/acceleration dynamics. The results indicated no significant correlation between either weighted or unweighted total SA scores and overall takeover performance. However, the weighted SA score was significantly higher than the unweighted score in the "Roadworks" scenario, suggesting drivers attended more to high-relevance information. Analysis of individual questions revealed that specific knowledge, such as awareness of adjacent vehicles or affected lanes, correlated with earlier takeover times, while knowledge of speed limits correlated with later, smoother interventions. Furthermore, participants answered high-weighting questions more accurately than low-weighting ones, confirming that attention allocation aligns with perceived relevance. In the "Accident" scenario, attention focused primarily on the TOR cause, whereas in "Roadworks," it focused on surrounding vehicles. The study concludes that while weighting SAGAT questions does not currently improve the correlation with takeover performance metrics, it successfully reflects drivers' natural attention allocation strategies. The lack of correlation between total SA scores and performance suggests that current takeover metrics may be insufficient or that SA does not directly predict performance in these specific scenarios. The authors recommend future research using more sophisticated performance evaluations, such as the TOC-rating, and eye-tracking to further validate the adapted SAGAT method.
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
Ranked by relevance to this paper. Hover a topic for its definition.
- situational awareness
- mode awareness
- automation surprise
- situation awareness theory
- automation
- takeover transitions
Information type
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- Empirical Findings: behavioral performance data
- Theoretical Contribution: conceptual framework, theory or model