Effect of Circadian Rhythms and Driving Duration on Fatigue Level and Driving Performance of Professional Drivers
Bibliographic record
Abstract
Circadian rhythms, inherent in all humans, consist of 24-h biological patterns that affect a person's fatigue level. The effect of circadian rhythms on driving performance was explored in an on-road driving study. Fifteen middle-aged professional daytime drivers were recruited to participate in the experiment. Participants were classified into three groups: (a) a morning group that started driving at 09:00, (b) a noon group that started driving at 12:00, and (c) an evening group that started driving at 21:00. Each group completed a 6-h driving task. The self-reported Karolinska sleepiness scale score was recorded every 5 min, and data on driving performance parameters, such as steering and lane positioning, were also acquired. The results indicated that both circadian rhythms and driving duration had significant effects on self-reported fatigue levels and that the fatigue level increased faster in the evening group than the morning and noon groups. The results of the circadian rhythm analysis showed that a driver was most likely to feel tired between 14:00 and 16:00 and between 02:00 and 04:00, when the ability to stay within designated lane lines (lane maintenance) was significantly impaired for drivers in all three groups. The evening group drivers were the most at risk. The steering performance did not show a significant relationship with the self-reported fatigue level. The self-reported fatigue level is the result of the interactive effect of circadian rhythms and driving duration. The standard deviation of lane position was more correlated with circadian rhythms than with the steering reversal rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".