Examining the impacts of drivers' emotions on takeover readiness and performance in highly automated driving
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Summary
This study investigates the impact of drivers' emotional states on takeover readiness and performance in highly automated driving (SAE Level 3). While previous research has examined factors such as workload, traffic density, and age, the influence of emotion during the critical transition from automated to manual control remains largely unexplored. The authors address this gap by examining how specific emotions affect a driver's ability to resume vehicle control safely and effectively. The researchers conducted a human subject experiment using a desktop driving simulator with 24 participants. The study employed a within-subject design featuring four emotional conditions: angry, sad, happy, and calm. Emotions were induced by having participants watch two four-minute movie clips prior to each trial. The sequence of conditions was counterbalanced using a Latin square design to mitigate order effects. Near the end of each clip, a takeover request was issued, requiring participants to immediately assume control of the vehicle. After negotiating the driving scenario, participants returned control to the automated system and completed the Self-Assessment Manikin (SAM) survey to verify emotional states. The study recorded both objective driving metrics and subjective ratings of takeover readiness and performance. The results demonstrated that emotions significantly influenced takeover readiness and performance. The calm condition yielded the highest takeover readiness and the best performance, characterized by the smallest maximum longitudinal acceleration, the smallest maximum longitudinal jerk, and the largest minimum time to collision. Drivers in this state negotiated events smoothly and appropriately. In contrast, the anger condition resulted in the lowest takeover readiness and the most aggressive driving style. The study confirmed that the emotion elicitation was successful across all conditions. These findings highlight the vital role of emotional states in optimizing takeover performance in highly automated vehicles. The results have significant implications for the design of in-vehicle alert systems, suggesting that systems should adapt takeover request lead times based on the driver’s specific emotional state. By incorporating emotional factors into human-vehicle interaction models, this research contributes to enhancing safety and efficiency during the transition from automated to manual driving. The study underscores the need for adaptive systems that account for psychological states to mitigate risks associated with poor situational awareness and aggressive driving behaviors during takeover events.
Key finding
Calm emotional states led to the highest takeover readiness and best driving performance, while anger resulted in the lowest readiness and most aggressive driving behavior.
Methodology
simulator
Sample size: 24
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 openalex_abstract on 2026-05-08 (10 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-05 |
| archive | success | canonical_url | — | — | 6 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-09 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | partial | normalization | — | — | 10 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-05-05 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-09 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
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.
- takeover transitions
- automation
- acceptance adoption
- situational awareness
- affect mood
- 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
- Theoretical Contribution: conceptual framework, theory or model