A Framework for Determining Collision Likelihood Using Continuous Friction Values in a Connected Vehicle Environment
Why this work is in the frame
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Bibliographic record
Abstract
Jurisdictions currently provide information on winter road conditions through qualitative descriptors like bare and fully snow-covered. Ideally, these descriptors are meant to warn drivers beforehand about hazardous roads. In practice, however, discerning between safe and unsafe roads is sometimes unclear due to intermediate RSC classes covering too wide a range of conditions. This study aims at solving this safety ambiguity issue by proposing a framework for predicting collision likelihood within a road segment. The proposed framework converts road surface images into friction coefficients, which are then converted into continuous measurements through an interpolator. To find the best-performing interpolator, we evaluated geostatistical, machine learning, and hybrid interpolators. It was found that ordinary kriging had the lowest estimation error and was the least sensitive to changes in distance between measurements. After developing an interpolator, collision likelihood models were developed for segment lengths ranging from 0.5 km to 20 km. We chose the 6.5 km model based on its accuracy and intuitiveness. This model had 76.9% accuracy and included friction and AADT as predictors. It was also estimated that if the proposed framework were implemented in an environment with connected vehicles and intelligent transportation systems, it would offer significant safety improvements.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it