Voxel-Based Methodology for Automated 3D Sight Distance Assessment on Highways using Mobile Light Detection and Ranging Data
Bibliographic record
Abstract
Ensuring that the available sight distance (ASD) on highways meets the minimum requirements of geometric design standards is crucial for safe and efficient operation of highways. Current practices of ASD assessment using design software or through site visits are labor intensive, time consuming, and traffic disruptive. Thus, this paper introduces a fully automated algorithm that allows large-scale assessment of ASD in three-dimensional (3D) space on highways utilizing mobile light detection and ranging (LiDAR) data. The algorithm was tested on LiDAR data of highway segments in Alberta, Canada. The results showed that the algorithm was highly accurate in detecting sight distance limitations at the defined regions and, in all cases, the driver’s vision was restricted by the pavement surface on vertical crest curves. In the case of combined vertical and horizontal curves, the vertical crest curve was found to be the controlling element in sight distance deficiencies. In addition, the assessment of historical collision data revealed clusters along the regions defined with ASD limitations, indicating that restrictions in drivers’ vision could have contributed to the collision occurrence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".