Electroencephalography Sample Entropy of Driver Passive Fatigue Threshold in Automated Driving
DOI: 10.21203/rs.3.rs-457935/v1
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Summary
**Research Question and Motivation** This study addresses the safety risks associated with "passive fatigue" in automated driving, a state induced by insufficient mental workload rather than high task demand. As vehicle automation increases, drivers are often freed from active control, leading to under-arousal and degraded vigilance. The research aims to quantify the co-variation between task demand and mental workload, identify the specific time point at which passive fatigue occurs, and establish a physiological threshold for detecting this state using electroencephalography (EEG). **Methods** The researchers conducted a three-factor mixed experiment with 48 drivers (aged 20–35) who had no prior automated vehicle experience. The design included two between-subject variables: driving mode (automated vs. manual) and scenario complexity (monotonic vs. engaging), and one within-subject variable: six measurement stages over a one-hour session. Data were collected using a driving simulator, 64-channel EEG recordings, detection-response task (DRT) performance metrics, and subjective scales (NASA-TLX for workload and SOFI for fatigue). EEG data were preprocessed using EEGLAB, and sample entropy was calculated to assess signal complexity. ROC curve analysis was employed to determine the discrimination threshold for passive fatigue. **Findings** Results confirmed that drivers in automated driving under monotonic conditions experienced the lowest mental workload, placing them in the "reserve capacity region," yet reported significantly higher fatigue levels compared to other groups. This divergence between low workload and high fatigue validated the induction of passive fatigue. Performance degradation was time-dependent: DRT reaction times increased and accuracy decreased significantly in automated driving during stages 4 and 6 (approximately 40 minutes into the trial). EEG analysis revealed that alpha power increased significantly in the automated/monotonic group during these later stages. Crucially, the study identified an EEG sample entropy value of 0.243 as the critical threshold for distinguishing passive fatigue from the waking state, with an area under the ROC curve of 0.71, indicating high discriminative accuracy. **Significance** The study provides a precise physiological marker (EEG sample entropy > 0.243) and a temporal benchmark (~40 minutes) for passive fatigue in automated driving. It demonstrates that subjective workload measures alone are insufficient for real-time monitoring, whereas EEG entropy offers a viable objective metric. These findings have direct implications for the design of automated vehicle systems, suggesting that vigilance maintenance tasks or workload regulation strategies are necessary to prevent performance degradation and ensure safe takeover readiness. The research also highlights the need for future studies to explore driver self-regulation mechanisms and extend experimental durations to test the limits of fatigue control.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | cached | — | — | 4 | 2026-08-23 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.8-27b-gittensor | summ-v5 | 3 | 2026-08-23 |
| tag | success | vector_similarity | — | — | 11 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-09 |
Summary generated by qwen3.8-27b-gittensor on 2026-08-23; verification: pending re-verification.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- drowsiness detection algorithms
- drowsiness
- time on task
- truck driver fatigue
- situational awareness
- vigilance
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: physiological data
- Methodological Resource: validation psychometrics
- Theoretical Contribution: theory or model