Study on the Driving Risk Reduction in the Mountain Highway Tunnel Group under the Perspective of Visual Load
Bibliographic record
Abstract
More highway built in the mountains in recent years, the driving risk in the tunnel group is becoming a new issue. This paper analyzed the driving risk in the mountain highway tunnel group from the perspective of visual load. Based on vehicle test in the Pengshui‐Xiantang tunnel group in China, the evolution characteristics of MTPA were quantitatively analyzed, and the random forest model was constructed to discuss the effect factors of the maximum transient velocity value of the pupil area (MTPA) in different sections. The results are as follows: (1) The MTPA frequently presents a tendency of steep rise and fall in the tunnel group. MTPA in the second tunnel is significantly higher than the first tunnel. (2) The mountain tunnel group can be divided into nine sections; the velocity, design luminance, measured luminance, and location have different effects on MTPA in each section. Due to the complex terrain conditions, the location has a more significant impact on MTPA in the second tunnel. (3) The first tunnel entrance, the first tunnel exit to the second tunnel entrance, and the second tunnel exit are the areas with more significant pressure on drivers in the tunnel group. The visual load of drivers in the exit section of the last tunnel is the greatest. The driving risk reduction recommendations include improving the transition lighting design of the second tunnel, clarifying the tunnel group identification, and adding safety features at the tunnel connection section, in order to clarify the driver’s expectations and reduce the fear of the unknown mountain environment.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".