Multidimensional Assessment of Takeover Performance in Conditionally Automated Driving
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Summary
This study investigates the multidimensional assessment of driver takeover performance in conditionally automated driving, specifically examining how Situational Awareness (SA) and Spare Capacity (SC) influence response efficiency, user experience, and driving safety. The research addresses the critical challenge of ensuring safe and comfortable control transitions when automated systems encounter operational boundaries. While SA is widely recognized as a factor in takeover success, the authors argue that takeover performance is multifaceted and potentially conflicting (e.g., speed vs. quality). Consequently, the study aims to systematically evaluate the distinct and complementary roles of SA and SC, derived from Task-Capability Interface theory, in predicting various takeover metrics. The researchers conducted a driving simulator experiment with 57 participants across nine scenarios varying in traffic density and non-driving related tasks. Data collection included operational vehicle metrics, eye-tracking data for objective cognitive measures, and questionnaires for subjective assessments of SA, SC, and driver characteristics. The final dataset comprised 466 valid takeover events. To analyze the data, the authors employed Extreme Gradient Boosting (XGBoost) models to predict ten takeover performance metrics grouped into reaction times, subjective takeover qualities, and objective takeover qualities. These models compared baseline predictions using only driver characteristics against models incorporating SA, SC, or both, utilizing SHAP values and Bonferroni-adjusted significance tests to interpret feature contributions. The results reveal distinct patterns in how SA and SC affect different performance dimensions. For immediate reflexive responses, such as button press time and road reorientation time, SA played a dominant role; higher SA levels, particularly arousal and spare attention, significantly reduced reaction times. In contrast, for reflective responses like total takeover time, SC had a more substantial impact than SA, with lower perceived spare capacity leading to delayed conscious control resumption. Furthermore, SC demonstrated a greater overall impact on takeover quality than SA. Higher SC generally correlated with enhanced subjective ratings, including greater satisfaction and lower perceived risk, as well as improved objective execution trajectories. The study found that while SA drives speed, SC is critical for the quality and comfort of the takeover maneuver. These findings highlight the distinct yet complementary roles of SA and SC in shaping takeover performance. SA is primarily responsible for rapid initial responses, whereas SC determines the quality and safety of the subsequent control execution. This distinction offers valuable insights for optimizing human-vehicle interactions and enhancing automated driving system design. By understanding these cognitive factors, developers can better tailor takeover requests and system interfaces to support drivers in maintaining adequate situational awareness and spare capacity, thereby improving the safety and comfort of conditionally automated driving.
Key finding
Higher Situational Awareness shortens reaction times to takeover requests, especially reflexive responses, while Spare Capacity has a larger overall effect on takeover quality, raising both subjective ratings and objective trajectory measures. SA contributes minimally once DC and SC are already in the model.
Methodology
simulator
Sample size: 57 drivers, 466 takeovers analyzed
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 discover_arxiv on 2026-05-04 (4 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | arxiv | — | — | 3 | 2026-05-04 |
| archive | success | — | — | — | 1 | 2026-05-04 |
| 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 | partial | normalization | — | — | 2 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-05-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 16 | 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
- situational awareness
- automation
- automation surprise
- automation complacency bias
- temporal
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: conceptual framework, computational model