Comparison of Caffeine and Music as Fatigue Countermeasures in Simulated Driving Tasks
Bibliographic record
Abstract
Driver fatigue is a leading cause of traffic accidents. Appropriate fatigue countermeasures can help drivers prevent operational errors and improve road safety. A review of the literature has suggested that consuming caffeine and listening to music are two commonly adopted fatigue countermeasures. This paper examines effects of the two fatigue countermeasures on subjective fatigue levels and driving performance. Differences between the two methods are also investigated. Twenty participants completed three 120-minute control, caffeine, and music sessions at the same time on three days. Subjective fatigue levels were quantified using a subjective driver fatigue score, and driving performance was measured using 16 parameters related to vehicle control ability. Initially, there were no significant differences in subjective fatigue and driving performance among three sessions. The final subjective driver fatigue score for caffeine and music sessions was significantly lower than control sessions, suggesting both inhibited subjective fatigue increase. The increment in driving performance parameters for caffeine sessions was significantly less than control and music sessions, but not significantly different between control and music sessions. Caffeine is more effective than music in inhibiting driving performance deterioration; because caffeine stimulates the central nervous system; on the other hand, music only reduces boredom and may introduce additional distraction to the driver.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".