Plateau effect on driver’s hazard perception response mode: Graph construction approach
Why this work is in the frame
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Bibliographic record
Abstract
It is crucial for drivers to conduct rapid and effective risk perception and response processes when faced with hazardous driving situations. The low pressure and oxygen environment in the plateau results in a greater workload of drivers, contributing to a significant decline in perception and response ability. This study proposes a graph construction approach to model drivers’ hazard response modes (HRMs) in plateau areas. A total of 31 drivers (23 males) aged 21 to 55 years (M [age] = 28.0 years, M [driving experience] = 6.5 years) were recruited to participate in four hazard perception experiments using a UC-WIN/ROAD driving simulator. The experiments were successively conducted in five cities with different altitudes, including Nanjing (50 m), Nyingchi (2,995 m), Lhasa (3,650 m), Nagqu (4,460 m), and Yanghu Scenic Spot (4,998 m). Then, according to the graph construction approach, four HRMs for drivers were extracted. In addition, two series of generalized linear models were proposed to analyze the relationships between the perception reaction time (PRT), HRM, altitude, age, acclimation period, gender, and driving experience. The effects of significant variables, including scenario types, altitude, acclimation period, driving experience, and gender, were used in the construction of HRM and risk perception ability of plateau drivers. These results showed that constructing HRMs to model the driving styles of plateau drivers is feasible and effective, enabling future driving assistance systems to be better customized for drivers in such a particular condition.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it