Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures
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Summary
This review article addresses the safety challenges associated with automated-to-manual control transitions in automated vehicles, specifically focusing on the "take-over" process where drivers must resume control from automation. The motivation stems from significant safety issues in higher levels of automation (SAE Levels 2–4), where drivers often face complex transitions with little warning. The authors aim to identify factors influencing take-over performance and determine which driver models can accurately simulate these behaviors to assist in system design and safety assessment. The study employs a systematic literature review methodology, searching five databases (TRID, Compendex, Scopus, Web of Science, and Google Scholar) for peer-reviewed articles published from 2012 onward. The review is divided into two parts: empirical studies of automated vehicle take-overs and quantitative driver modeling. Inclusion criteria required empirical reporting on control transitions for the former and the development or enhancement of models predicting driver behavior relevant to take-over phases for the latter. This process yielded 83 articles on automated driving take-overs and 60 articles on driver modeling. The authors analyzed these studies to identify influential factors on take-over time and post-take-over control quality, and to evaluate the suitability of existing models for simulation. The review identifies several factors significantly influencing take-over performance. Take-over time is primarily affected by the time budget (time-to-collision), repeated exposure to take-overs, silent failures, and handheld secondary tasks. Post-take-over control quality is significantly impacted by these factors as well as take-over request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol impairment. A key finding is that drivers respond similarly to manual emergencies and automated take-overs, albeit with a delay, suggesting that existing braking and steering models for manual driving may be applicable to automated scenarios. The authors note that longer time budgets generally lead to longer take-over times, with meta-analysis indicating a 0.27-second increase in take-over time per one-second increase in time budget. The significance of this work lies in its recommendations for computational simulations. The authors conclude that evidence accumulation models are promising for capturing the identified effects on driver behavior. By integrating these models with pre-crash kinematic data, designers can simulate safety outcomes and assess the impact of design choices without relying solely on real-world testing. This review provides stakeholders with a consolidated understanding of take-over dynamics and model selection criteria, facilitating the development of safer automated vehicle systems.
Key finding
Drivers respond similarly to manual emergencies during automated take-overs albeit with a delay, suggesting existing braking and steering models for manual driving may be applicable.
Methodology
review
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- takeover transitions
- automation
- manual
- automation surprise
- situational awareness
- automation complacency bias
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
- Theoretical Contribution: computational model, conceptual framework