Influence of Vertical Alignment on Horizontal Curve Perception: Phase I: Examining the Hypothesis
Bibliographic record
Abstract
The importance of the driver’s receiving precise visual cues from the road environment cannot be overstated. If the visual cues are confusing or in any way cause the driver to incorrectly assess the approaching road environment, the crash risk of the driver may increase. Of particular concern are the perceptual problems induced by superimposing horizontal and vertical curves. To investigate the effect of overlapping vertical alignment on the perceived horizontal curvature, dynamic and static computer-generated three-dimensional presentations of the driver’s view of a road were created. In Phase I of the experiment, data were collected to test the hypothesis that overlapping crest curves made the horizontal curvature appear sharper and overlapping sag curves made the horizontal curvature appear less sharp. The results of both presentation methods (dynamic and static) were in agreement and showed that the hypothesis was valid. However, the hypothesis was more evident in the case of sag curves, which is a more serious issue with respect to safety. The probability of erroneous perception, as influenced by vertical curves, increases as ( a) the sight distance increases, ( b) the horizontal curve radius increases, and ( c) the length of vertical curve per 1% change in grade decreases. Driver characteristics did not appear to affect the horizontal curve perception.
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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 | 0.004 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".