Transformer-Based Classification of Road Conditions Using Vehicular Sensor Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Precise classification of road surface types is a crucial aspect of current advanced transport systems due to its importance in safety, routing, and prognostic maintenance. This work proposes a transformer model to predict the road surface types (paved or unpaved) based on vehicular sensor data derived from several onboard sensors such as accelerometers, gyroscopes, magnetometers, and temperature sensors. The methodology involves a feature selection step based on the Random Forest classifier ranking without negatively affecting the predictive accuracy. An initial transformer design was furthermore developed with multi-head attention, which was trained and validated on time-series data with regularization. The proposed model achieved a weighted average F1-score of 0.97, which supports the use of transformers in analyzing vehicular sensors and their application in the field of smart transportation.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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