Impact of Tunnel Groups on Pupil Diameter of Drivers on Mountainous Freeway in China: A Real-World Driving Study
Bibliographic record
Abstract
A real-world driving experiment was performed in the Wen-Ma section of the G4217 Rong-Chang Freeway situated in the Sichuan Province to investigate the impact law of the pupil diameter of drivers in tunnel groups on the mountainous freeway. The eye-movement data of drivers were collected, and the percentage of pupil diameter variable (PPDV) was used as a visual characteristic index. The analysis of the overall change in the PPDV of drivers in the experimental sections demonstrated that the PPDV in tunnel groups differed significantly between the nontunnel sections and single tunnel sections. Subsequently, a related model for the PPDV of drivers and the length of the connecting zone between tunnels was established, its reliability evaluated, and the smooth mutation value obtained on the basis of the mutation theory. Thereafter, a tunnel group definition standard based on the visual effect of drivers was developed. A six-zone approach was devised for the analysis of tunnel groups, and the result revealed that the different zones in the tunnel group have different impact on PPDV of drivers. The results revealed that the different zones of tunnel group have different impact on PPDV of drivers. Furthermore, lighting transition facilities should be set in the exit section of tunnel. The PPDV of drivers was negatively correlated with the length of the connecting zone of tunnel groups, and 100 m is the recommended safety length threshold for the connecting zone of tunnel groups.
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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 | 0.001 |
| 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 | 0.000 | 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".