Are Electrodermal Activity-Based Indicators of Driver Cognitive Distraction Robust to Varying Traffic Conditions and Adaptive Cruise Control Use?
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Summary
This study investigates whether electrodermal activity (EDA) indicators reliably reflect driver cognitive distraction amidst varying traffic conditions and adaptive cruise control (ACC) use. Motivated by the need for robust driver state monitoring in semi-autonomous vehicles, the authors examined if EDA signals capture mental workload changes driven solely by distraction or also by environmental complexity and automation. Using a driving simulator, 28 participants completed six scenarios combining two levels of cognitive distraction (mental arithmetic task vs. none) and three levels of environment complexity (traffic density and road construction). Participants could freely engage or disengage ACC. Researchers analyzed three EDA metrics: mean skin conductance level (SCL), skin conductance response (SCR) amplitude, and SCR rate. Linear mixed models assessed the impact of distraction, environment, and ACC on these indicators. Results showed all three EDA indicators significantly increased with cognitive distraction and decreased when ACC was engaged, indicating reduced mental workload with automation. Environment complexity significantly affected SCL and SCR amplitude but not SCR rate. Specifically, SCL decreased while SCR amplitude increased in the most complex environment (high traffic with construction zones). No significant interactions were found between the factors. The findings demonstrate that EDA indicators are sensitive to mental workload fluctuations caused by distraction, environment, and automation, rather than distraction alone. This suggests EDA can support adaptive automation systems that dynamically adjust assistance based on driver state and context. However, the differential sensitivity of indicators to environmental factors highlights the need for further research to optimize monitoring strategies for safety and trust.
Key finding
All three EDA indicators were significantly influenced by cognitive distraction and ACC use, while environment complexity influenced SCL and SCR amplitude but not SCR rate, suggesting EDA indicators reflect mental workload variations from multiple sources.
Methodology
lab_experiment
Sample size: 29
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 | 2026-06-07 |
| 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 | 2026-06-07 |
| tag | success | vector_similarity | — | — | 17 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-05-08 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-07; verification: verified.
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- Empirical Findings: physiological data
- Methodological Resource: validation psychometrics, tool software