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Record W2979741866 · doi:10.1139/cjce-2019-0024

Nighttime visual recognition performance of light emitting diode traffic signs

2019· article· en· W2979741866 on OpenAlexvenueno aff
Zhonghua Wei, Jichao Xu, Shaofan Wang, Sheng Liu, Shi Qiu

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldPsychology
TopicSafety Warnings and Signage
Canadian institutionsnot available
Fundersnot available
KeywordsLuminanceTraffic signSign (mathematics)Computer scienceSymbol (formal)Computer visionFeature (linguistics)Traffic sign recognitionCharacter (mathematics)Artificial intelligenceMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.009
GPT teacher head0.209
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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