Research on driving fatigue detection model in plateau based on steering wheel indicators
Bibliographic record
Abstract
The low-pressure and low-oxygen environment in plateau regions significantly increases drivers’ psycho-physiological stress, affecting their driving behavior and road safety. This study constructed a simulation model using the UC/win-road simulator to replicate a plateau highway scenario. A fatigue detection model was also developed based on characteristic parameters from the steering wheel indicator. Twenty-six drivers (18 males, 8 females) from Lhasa, located at an altitude of 3650 m, participated in a simulation experiment. Heart rate variability (HRV) was chosen as the characteristic parameter from electrocardiogram signals to assess driving fatigue. Previous studies helped determine a threshold range, defining three fatigue states in the simulation: alertness, mild fatigue, and severe fatigue. Ten steering wheel angle indicators were extracted for time and frequency domain feature analysis. One-way ANOVA and collinearity tests identified feature indicators showing significant differences between fatigue states, which were then used as characteristic parameters for fatigue detection. An ordered logistic model was used to build the driving fatigue detection model. The driver’s fatigue state, determined by HRV, was the dependent variable, while steering wheel indicator parameters served as independent variables. The research results provide technical support and serve as a reference method for real-time detection of driving fatigue in plateau regions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".