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 takeover request lead time, workload, and traffic density, the influence of emotion remains largely underexplored, particularly during the critical transition period when drivers must resume control. The authors address this gap by examining how specific emotions affect a driver’s ability to safely and effectively take over vehicle control, building on prior findings that negative emotions like anger and anxiety impair manual driving performance. 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. 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 sequence of conditions was counterbalanced using a Latin square design to mitigate order effects. Data collected included objective measures of takeover driving behavior and subjective ratings of readiness and performance. The results demonstrated that emotions significantly influenced both takeover readiness and performance. The calm condition yielded the highest readiness and best performance, characterized by the smallest maximum longitudinal acceleration and jerk, as well as 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. These findings confirm that emotional state is a critical determinant of how effectively a driver can transition from passive monitoring to active control. The significance of these findings lies in their implications for the design of in-vehicle alert systems and human-automated vehicle (AV) interaction models. The study suggests that takeover request systems should be adaptive, adjusting lead times based on the driver’s specific emotional state to optimize performance. By incorporating emotional states into interaction models, developers can enhance safety and efficiency during takeover transitions. This research contributes to the broader field of human factors in automated driving by highlighting the necessity of accounting for psychological variables, not just cognitive workload or situational awareness, in designing robust automation systems.
Key finding
Drivers in a calm emotional state demonstrated the highest takeover readiness and best driving performance, whereas anger led to 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 unpaywall on 2026-05-06 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 3 | 2026-05-29 |
| archive | success | canonical_url | — | — | 16 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| 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 | success | openalex | — | — | 2 | 2026-05-08 |
| promote | success | — | — | — | 1 | 2026-05-06 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 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
- acceptance adoption
- situational awareness
- driverless ads
- 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