Assessing Roadside Safety With Computer Vision: FHWA Ratings as the Key Predictor of Rural Road Departure Crashes and Severity
Bibliographic record
Abstract
This is the first study to evaluate the effectiveness of the Federal Highway Administration (FHWA) roadside safety rating system in predicting Road Departure (RD) crashes on rural roads. The research employs a two‐step framework: first, a computer vision model was used to extract detailed information on clear zones, rigid obstacles, side slopes, and safety barriers from roadway images. Next, the extracted data was integrated with crash records for statistical analysis. The FHWA safety rating system, which combines these features, shows a significant correlation with rural RD crash frequency and severe injury rates, as confirmed by Spearman correlation coefficients. Furthermore, using the negative binomial regression model, the safety rating emerged as the strongest predictor of rural RD crashes and their severity compared to individual roadside features, underscoring its value in assessing crash risk. With its seven categories, the FHWA safety rating system provides a more comprehensive predictor of rural RD crash risk, making it an essential tool for identifying high‐risk locations and prioritizing safety interventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".