Driving impairment detection due to sun exposure and contrasting shadow of surface objects: an urban case study
Bibliographic record
Abstract
A change in the driver’s vision quality might result in deterioration of their perception and effectiveness. The lack of an integrated algorithm to distinguish the location of driver vision impairment motivated this study. The proposed model benefits from a combination of several sub-algorithms such as sun positioning, glare detection, and contrasting shadow illustrator derived from raw geospatial data. The methodology is implemented through a case study involving a road network in a large metropolitan area, a digital elevation model, the associated hillshade geographic data, and weather data from Montreal, Canada. The methodology and corresponding data analysis were implemented in Python. The results revealed a geospatial model to estimate the boundaries of transition points between the glare and contrasting shadows created by changes in the roadway surroundings. The results provided by the model can be used as a tool to aid decision-makers in new road construction and urban planning by creating safety countermeasure strategies and design review of road geometry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".