Automated Extraction of Horizontal Curve Attributes using LiDAR Data
Bibliographic record
Abstract
Horizontal curves are designed to provide a safe and smooth transition between straight segments on a highway network. Although curves are often designed to meet very stringent standards, imperfections during construction and high operating speeds mean that they are still prone to collisions. Therefore, it is essential that attributes of curves are surveyed to ensure they meet design requirements. Moreover, knowledge of the locations of horizontal curves and their attributes is also required to provide drivers with accurate information in advanced curve-warning systems, which are expected to enhance safety. Unfortunately, conventional techniques to obtain information about horizontal alignments are extremely tedious and, in some cases, impractical. This paper proposes a method by which horizontal curves can be automatically detected and their attributes automatically measured on scans of the highways obtained using light detection and ranging (LiDAR) technology. The proposed method is tested on two different highway segments at the Province of Alberta, Canada, where LiDAR data were collected. Moreover, testing was also conducted using virtual highways with curves with known attributes generated in AutoCAD Civil 3D. The results show that the code is successful in detecting all curves on a highway segment; moreover, the attributes of those curves were estimated with a high degree of accuracy (average difference <3%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".