Assessment of driver fatigue-related brain responses and causal factors during driving under different traffic conditions
DOI: 10.3389/fams.2024.1426253
archive: archived pipeline: cataloged verified
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Summary
This study investigates the causal relationship between driver fatigue, brain activity, and specific workload factors (mental and physical) across different traffic environments. Despite advancements in Intelligent Transport Systems, fatigue-related accidents remain prevalent. While previous research has linked fatigue to biomarkers like electroencephalography (EEG), the specific contributions of varying traffic conditions to mental and physical workload, and their subsequent impact on brain activity, remain unclear. The authors aimed to clarify how cognitive and operational loads inherent in different driving environments induce fatigue and manifest in EEG spectral responses. The researchers conducted driving simulator experiments with 12 male participants, aged 19–23. Participants underwent 30-minute driving sessions in three distinct scenarios: urban driving with 20% traffic density, congested driving with 100% traffic density, and highway driving with 20% traffic density. Each condition was tested on separate days. Data collection included 9-channel EEG recordings, subjective workload assessments using the NASA-TLX scale, fatigue questionnaires, and objective measures of driving operations and perceptual targets derived from video analysis. EEG analysis focused on theta (4–8 Hz) and alpha (8–13 Hz) band spectral changes between the initial and final two minutes of each session, as these bands are associated with fatigue. Results indicated significant differences in fatigue-related EEG responses across conditions. Urban driving elicited the largest increase in theta- and alpha-band activity, followed by congested driving, while highway driving showed a decrease in these bands. Subjective workload assessments revealed that urban and congested driving induced higher mental demand and effort compared to highway driving, while congested driving resulted in the highest physical demand. Objective data showed that urban driving required the highest total number of operations and perceptual tasks, whereas congested driving involved high operational load but low perceptual load, and highway driving involved high perceptual load but low operational load. Strong correlations were found between EEG spectral changes, subjective workload, and the combined number of operations and perceptions. Self-reported fatigue levels were significantly higher in urban driving than in highway driving. The study concludes that the accumulation of both mental and physical workload, driven by the specific perceptual and operational demands of a traffic environment, is a primary cause of driver fatigue. Urban driving, which requires simultaneous high levels of perception and operation, induces the greatest fatigue. In contrast, congested driving primarily induces physical stress through frequent operations, while highway driving induces mental stress through continuous perception. These findings suggest that monitoring the specific cognitive and operational loads of driving environments is crucial for understanding and preventing fatigue-related accidents.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-17 |
| archive | success | unpaywall | — | — | 2 | 2026-06-25 |
| extract | success | cached | — | — | 2 | 2026-06-25 |
| clean | success | clean | — | — | 1 | 2026-06-18 |
| chunk | success | chunk | — | — | 1 | 2026-06-18 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-18 |
| promote | success | — | — | — | 1 | 2026-06-17 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-25 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-18 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-25; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- stress driving
- drowsiness
- mental demand
- drowsiness detection algorithms
- truck driver fatigue
- time on task
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
- Theoretical Contribution: theory or model