Nighttime visual recognition performance of light emitting diode traffic signs
Bibliographic record
Abstract
Light emitting diode (LED) traffic signs have been deployed on many urban roadways recently. However, a lack of uniform engineering criteria for the luminance level of LED traffic signs in China brought up consequences such as inaccurate recognition of signs for drivers as well as potential traffic accidents. This paper explores the possible luminance range of LED traffic signs through full-scale field static experiments. Visual recognition distance, character height, character stroke, sign background color, sign dimension, and sign complexity degree are taken into account. The visual recognition luminance value required for drivers are recorded. Experiment results reveal that: for character symbol, the background color of the board and character strokes have no significant effect on driver’s visual recognition performance; for graphic symbol, the higher of graphic complex degree, the higher luminance value required for driver to recognize. This study summarizes the luminance thresholds of different kinds of LED signs for different roadway classes. The findings of this study would provide reference for future design of LED traffic signs.
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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 | 0.002 |
| 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) | 0.003 | 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".