Automated Assessment of Passing Sight Distance on Rural Highways using Mobile LiDAR Data
Bibliographic record
Abstract
Providing sufficient Available Sight Distance (ASD) that meets the minimum design requirements is crucial for highway safety. Previous work on sight distance assessment focused on Stopping Sight Distance (SSD) with little attention given to Passing Sight Distance (PSD). Insufficient PSD could lead to severe collisions such as head-on and sideswipe crashes. To address this gap, this paper introduces an automated method for PSD assessment on two-lane highways using mobile Light Detection and Ranging (LiDAR) data. The procedure involved extracting centerline lane marking, defining passing-allowed and passing-prohibited regions, computing the ASD, and comparing the existing centerline marking pattern (i.e., passing and no-passing zones) to a proposed lane marking that is based on the ASD for passing maneuvers. Regions that meet the design standards, substandard zones, and non-optimal design regions were all defined. A reallocation of PSD zones was conducted based on the ASD including modifying the existing lane marking pattern, which resulted in increasing the total length of passing zones by up to 20%, providing more, but safer, passing opportunities. A high-level safety assessment of historical collisions showed clusters of crashes along regions where passing is currently allowed at locations where the ASD is less than standard requirements. The proposed framework represents a tool by which transportation agencies could assess PSD, upgrade the design of existing highways, and investigate the consequences of PSD limitations to ensure compliance with standards during highway service life.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".