Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving
DOI: 10.1016/j.trc.2020.01.006
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Summary
This study investigates how emotional valence (positive vs. negative) and arousal (high vs. low) influence driver takeover performance in conditionally automated (SAE Level 3) driving. While previous research has examined factors like takeover lead time and non-driving-related tasks, the impact of emotion on the transition from automated to manual control remains underexplored. The authors hypothesized that positive valence would improve takeover quality by broadening attention and cognitive resources, while the benefit of high arousal on response time—observed in manual driving—would be diminished in automated contexts due to the reflexive nature of takeover requests. The researchers conducted a within-subjects driving simulation experiment with 32 university students. Participants experienced four takeover events while watching movie clips designed to induce specific emotional states across the valence-arousal spectrum (e.g., sad, angry, happy, calm). Takeover performance was measured by timeliness (time from request to maneuver start) and quality, assessed via maximum resulting acceleration, maximum resulting jerk, and minimum time to collision. Data from 31 participants were analyzed using mixed linear models. Results indicated that emotional valence significantly affected takeover quality but not timeliness. Positive valence led to smoother maneuvers, characterized by significantly smaller maximum resulting acceleration and jerk, supporting the hypothesis that positive emotions enhance driving smoothness and ride comfort. Conversely, neither valence nor arousal had a significant effect on takeover time or minimum time to collision. Contrary to findings in manual driving where high arousal improves hazard response speed, high arousal did not yield faster takeover times in this study. However, a non-significant trend suggested high arousal might contribute to better takeover quality through reduced acceleration. The study concludes that the benefits of positive emotions carry over from manual to conditionally automated driving, improving the safety and comfort of takeover transitions. In contrast, the advantages of high arousal regarding response speed do not translate to automated driving contexts, likely because takeover requests serve as explicit attention cues that override the need for arousal-driven vigilance. These findings suggest that in-vehicle systems could potentially improve safety by monitoring and regulating driver emotions, such as mitigating negative states before requesting control. The authors note limitations, including the use of a low-fidelity simulator and a single type of takeover scenario, recommending future research with higher fidelity and diverse event types.
Key finding
Positive emotional valence improved takeover quality (lower maximum acceleration and jerk) in conditionally automated driving, but high arousal did not produce faster takeover times.
Methodology
simulator
Sample size: N=32
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 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 5 | 2026-05-28 |
| 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 | success | — | — | — | 1 | 2026-05-04 |
| promote | success | — | — | — | 1 | 2026-05-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 17 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Empirical Findings: behavioral performance data
- Theoretical Contribution: conceptual framework, theory or model