Applicability of risky decision-making theory to understand drivers' behaviour during transitions of control in vehicle automation
DOI: 10.5151/ergodesign2019-2.09
archive: archived pipeline: cataloged verified
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Summary
This theoretical paper addresses the challenge of ensuring safe transitions of control in automated vehicles, specifically focusing on drivers’ behavior when resuming control from automation. The authors identify a critical gap in current research: while studies often correlate visual attention with performance, they rarely explain how drivers use acquired visual information to make safe decisions during these transitions. Motivated by the "out of the loop" phenomenon—where drivers disengaged from driving tasks lose situation awareness (SA)—the paper proposes applying risky decision-making theory to model and understand this process. The study employs a theoretical framework rather than empirical experimentation. It synthesizes concepts from human factors engineering, specifically Situation Awareness recovery, with principles from decision-making theory. The authors compare rational decision-making models with risky decision-making models, arguing that the latter are more applicable to driving transitions due to inherent uncertainties, time pressure, and information overload. Key risky models discussed include evidence accumulation, bounded rationality, and satisficing. The paper maps the stages of Endsley’s Situation Awareness model (perception, comprehension, projection) onto the stages of decision-making (problem definition, solution identification, value assessment, choice), positing that recovering SA is analogous to accumulating evidence to reduce uncertainty. The primary finding is that the process of evidence accumulation in risky decision-making models shares strong parallels with the recovery of Situation Awareness. The authors argue that drivers, facing barriers such as attention tunneling and change blindness, cannot achieve rational, optimal decisions. Instead, they engage in risky decision-making, often "satisficing" by selecting the first acceptable action (e.g., braking without steering) when time is limited. Furthermore, the paper highlights that visual attention and decision-making are tightly coupled; selective gaze fixation biases the evidence accumulation process, thereby influencing the final choice. This suggests that drivers’ take-over behavior is not just a reaction to time pressure but a result of how they sample and prioritize visual information under uncertainty. The significance of this work lies in its implications for the design of human-machine interfaces in automated vehicles. By treating take-over behavior through the lens of risky decision-making, researchers and designers can better understand which specific information drivers use to achieve safe transitions. The authors conclude that evidence accumulation models can serve as tools to quantify the link between information acquisition and response probability. This knowledge can inform the development of more human-centered in-vehicle interfaces that support drivers in accumulating sufficient evidence quickly, thereby enhancing safety during transitions of control.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-06 |
| archive | success | unpaywall | — | — | 2 | 2026-06-09 |
| extract | success | pdftotext | — | — | 2 | 2026-06-09 |
| clean | success | clean | — | — | 1 | 2026-06-09 |
| chunk | success | chunk | — | — | 1 | 2026-06-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-09 |
| enrich | success | semantic_scholar | — | — | 1 | 2026-06-09 |
| promote | success | — | — | — | 1 | 2026-06-06 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-09 |
| tag | success | vector_similarity | — | — | 8 | 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.
- situational awareness
- automation
- takeover transitions
- automation surprise
- automation complacency bias
- mode awareness
Information type
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- Theoretical Contribution: theory or model, conceptual framework, computational model