Analysis of Drivers’ Workload States on Highways in High Elevation Regions
Bibliographic record
Abstract
The low-pressure (and low-oxygen) environment in plateau regions affects drivers’ psychophysiological states and, thus, driving behavior and road safety. Six scenarios of expressway exit ramps were constructed based on the UC/win-road simulator to quantitatively analyze the effect of the plateau environment on workload state. A total of 50 participants in Nanjing (altitude 50 m) and 50 participants in Lhasa (altitude 3,650 m) were recruited for the simulation experiments. Based on the principal component analysis, a sample entropy index (SEI) was constructed to reflect the drivers’ psychophysiological levels by combining the sample entropy of electroencephalogram (EEG) signals β%, β/(α+θ), β/α, and heart rate (HR). Meanwhile, the pupil area change rate and the percentage of fixation time and duration within the region of interest were analyzed to characterize the drivers’ visual workload. Based on the psychophysiological and visual load, the differences in workload states among plain and plateau drivers were analyzed using a regression model and a one-way analysis of variance (ANOVA) test. At the same time, the changing trends of the drivers’ workload states in the curved section of the exit ramp under different scenarios were analyzed. The plateau drivers experienced a higher level of mental stress and greater psychological load. Furthermore, the higher radius of horizontal curves reduced the visual load level for drivers when traveling on the highway exit ramp sections. These findings should serve as a valuable reference for refining the design of plateau expressway exit ramps and providing a theoretical basis for improving driving safety in plateau areas.
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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 | 0.000 | 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".