The Integrative Time-Dependent Modeling of the Reliability and Failure of the Causes of Drivers’ Error Leading to Road Accidents
Bibliographic record
Abstract
Nowadays, the human error is usually identified as the conclusive cause of investigations in road accidents. The human although is the person in control of vehicle until the moment of crash but it has to be understood that the human is under continued impact by various factors including road environment, vehicle and human’s state, abilities and conduct. The current advances in design of vehicle and roads have been intended to provide drivers with extra comfort with less physical and mental efforts, whereas the fatigue imposed on driver is just being transformed from over-load fatigue to under-load fatigue and boredom. A representational model to illustrate the relationships between design and condition of vehicle and road as well as driver’s condition and state on fatigue and the human error leading to accidents has been developed. Thereafter, the stochastic mathematical models based on time-dependent failure rates were developed to make prediction on the road transportation reliability and failure probabilities due to each cause (vehicle, road environment, human due to fatigue, and human due to non fatigue factors). Furthermore, the supportive assessment methodology and models to assess and predict the failure rates of driver due to each category of causes were developed and proposed.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".