Effect of Vertical Alignment on Driver Perception of Horizontal Curves
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The perception of the driver of the road features ahead is an important human factor that can considerably affect traffic safety and design consistency, and should be addressed in road design. An erroneous perception of the road can lead to actions that may compromise traffic safety. Previous studies have shown that combined horizontal and vertical alignments can cause a wrong perception of the horizontal curvature. In this paper, the hypothesis that the perception of the driver of the horizontal curvature is affected by the overlapping vertical alignment is examined analytically. Computer animation was selected as a three-dimensional presentation method of the road perspective, and was found to produce a realistic view of the road. A sample of drivers was interviewed to determine the radius of a horizontal curve on a level grade that would look equal to a radius of a horizontal curve overlapping with a vertical curve. The statistical analysis showed that the horizontal curvature looked consistently sharper when it overlapped with a crest curve and consistently flatter when it overlaps with a sag curve. Field measurements of operating speed profiles on a selected sample of combined alignments confirmed that, for the selected sample of alignments, driver behavior on horizontal curves depended on the overlapping vertical curve rather than the vertical grade of the approach tangent.
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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 | 0.000 | 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 it