Potential of Highly Automated Vehicles for Monitoring Fatigued Drivers and Explaining Traffic Accidents on Motorway Sections
Bibliographic record
Abstract
The near-future deployment of high-level automation vehicles ( AVs ) can render promising opportunities to solve ongoing hindrances in modern safety-related research. Monitoring fatigued drivers on any road section is one of these challenges. Vehicle trajectory big data, monitored through AVs , include key information with which to monitor fatigued drivers on roads. To mine this upcoming opportunity, a new data-driven approach which allows the direct monitoring of fatigued drivers on road segments is proposed here for the first time. A feasible study was conducted using big vehicle trajectory data and real-life traffic accident data. The results showed that fatigued drivers on a target road section can be successfully surveyed using the driving durations from departure locations to the target road section. It was found that, with a statistical correlation of 0.90, an index for fatigued drivers has strong explanatory power about the traffic accident rate. This finding indicates that the proposed method will be a promising means by which to monitor fatigued drivers at road locations in the upcoming era of autonomous vehicles. In addition, the method is immediately practicable if vehicle trajectory data are available.
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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 | 0.000 | 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".