Mitigating Truck Driver Fatigue: A Driver Sleepiness Detecting System
Bibliographic record
Abstract
Sleep deprivation among truck drivers is a pervasive issue due to the demanding nature of their job, involving long hauls covering extensive distances both during the day and night. The resulting weariness and drowsiness significantly contribute to major accidents on roadways. A study conducted by the Central Road Research Institute (CRRI) revealed that fatigued drivers who fell asleep at the wheel caused 40% of traffic accidents on the 300 Km Agra-Lucknow Motorway in 2022, with Uttar Pradesh recording a high number of fatalities. These alarming statistics raise concerns about the apparent disregard for the importance of sufficient rest among Indian highway drivers, leading to life-threatening situations. In a nation where traffic accidents claim a life every three minutes, it has become paramount to find a viable solution. To address this pressing issue, we propose a novel driver sleepiness detecting system. This system is designed to recognize and alert drivers when they are drowsy or on the verge of falling asleep, thereby preventing potential accidents. By providing timely warnings and promoting increased awareness of their sleep status, this technology aims to mitigate the risks associated with fatigue-induced accidents, ultimately safeguarding lives and promoting road safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".