A Fully Automated Approach to Extract and Assess Road Cross Sections From Mobile LiDAR Data
Bibliographic record
Abstract
Road cross sections are designed to ensure safe operation of highways. Tangent segments are typically designed with cross slopes to ensure efficient drainage of water off the road's surface, likewise, on horizontal curves the cross section is superelevated (tilted) to help vehicles counteract centrifugal forces. In both cases, ineffective slopes that do not meet design requirements, put vehicles at risk of overturning and skidding. Similarly, if deficiencies exist in side slopes, the chance of recovery for vehicles that run-of-the-road decreases substantially. Thus, transportation agencies must constantly assess elements of a road's cross section to ensure that they meet current design standards throughout their service life. The microscopic nature of cross sectional elements makes measuring such information time consuming, highly disruptive to traffic, and resource intensive. To facilitate more efficient assessments of such features, this paper proposes a novel algorithm to extract road cross sections from light detection and ranging data. The algorithm involves estimating vectors which intersect the road's axis, whereby points within proximity to the vectors are retained and extracted. Slope information is then measured off the retained points. The proposed algorithm is fully automated and employs multivariate adaptive regression splines to identify locations of change in slope. The algorithm was tested on two highway segments in Alberta. The high efficiency and precise manner in which the slope data was extracted, demonstrates the value of using the proposed algorithm in performing network-level assessment of road cross sections.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".