Drivers’ Eye Movement Characteristics in a Combined Bridge-Tunnel Scenario on a Mountainous Urban Expressway: A Realistic Study
Bibliographic record
Abstract
Combined bridge-tunnel scenarios of driving on mountainous city expressways occur when bridges and tunnels frequently alternate during driving. The complex nature of these driving scenarios imposes crucial requirements on the drivers’ eye movement characteristics. This paper attempts to clarify these characteristics using descriptive statistics and the box graph method, registering the pupil diameter, blink duration, fixation, saccades, and fixation loci at different tunnel locations, bridges, and ramps. Realistic driving experiments were performed on the road segment spanning from the Nanchang tunnel to the Liujiatai tunnel freeway in Chongqing, China. Eye movement data were collected for 21 drivers. The experimental results showed that, while driving in the tunnel, the maximal pupil diameter of the participating drivers was approximately 4.0 mm as the driving mileage and the number of tunnels increased, and the maximal visual load on the drivers in the tunnel tended to be stable. At the second tunnel exit, the ramp, the middle section of the first bridge, and the third tunnel exit, the driving load was the highest, while the fixation duration was shorter for nighttime driving. The fixation duration was the longest for the diversion road of bridge B1 to the ramp during the day, and the fixation times were the longest at the beginning and end of the test road. The drivers more often paid attention to the speed dashboard while entering tunnels during daytime driving (compared with nighttime driving).
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".