The Investigation of Visual Characteristics of High‐Speed Railway Drivers: Perspectives of Light Environment
Bibliographic record
Abstract
High‐speed railway (HSR) operations heavily rely on visual inputs, yet there is a notable gap in examining how HSR drivers adjust their eye movements in response to different lighting conditions, despite the pivotal role visual cues play in such environments. This investigation employed a Tobii Nano eye‐tracker to capture the visual behaviors of HSR drivers during simulated driving exercises. It centered on 4 areas of interest (AOIs): the front window, prompt area, dashboard, and speed dial. By using Train Sim World 3, we created 3 scenes (open section, short tunnel, and long tunnel) and utilized 4 key metrics (average pupil diameter, APD; time to first fixation, TFF; duration of first fixation, DFF; fixation duration, FD) to evaluate the variations in visual attention patterns of HSR drivers. The results reveal a significant relationship between these indicators and driving scenes. Drivers in tunnel settings tend to have a longer time to form fixations, reflected by longer TFF, duration of first fixation (DFF), and FD. Pupil dilation is most pronounced in tunnels with weaker light (long tunnels), while stronger light (short tunnels) leads to increased TFF, DFF, and FD. At the outset of the driving task, the front window and speed dial are the earliest fixated (earlier TFF). Throughout the driving, speed dial continues to be a central fixation, manifested by extended DFF and FD. Gaining insight into HSR drivers’ visual behaviors is essential for enhancing driving safety.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".