Analysis of dynamic available passing sight distance near right-turn horizontal curves during overtaking using LiDAR data
Bibliographic record
Abstract
The purpose of this study is twofold: to construct a model for measuring dynamic available passing sight distance (APSD) during overtaking using light detection and ranging (LiDAR) data, and to investigate the effects of several critical variables on dynamic APSD. The analysis considers a single passed vehicle with several variables, including vehicle dimensions Dp, initial headway IH, and speed differential SD. The proposed model simulates the passing process and dynamically estimates APSD considering two types of obstacles: (1) obstacles related to the road environment and (2) passed-vehicle obstacle which is critical especially on or near right-turn horizontal curves. Through multiple tests, the variation of dynamic APSD along with the passing maneuver is established. The results show that large Dp or long IH may increase the adverse effect of the passed vehicle on dynamic APSD. Either too large or too small SD may lead to a relatively high risk of head-on collisions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".