Precision enhancement of smartphone sensor-based pavement roughness estimation by standardizing host vehicle speed
Bibliographic record
Abstract
Condition assessment of pavement has a predominant part in delivering safety and comfort to users. Roughness is considered the most important characteristic as it affects road safety and vehicle operating costs. Authorities spend significant quantity of resources on using conventional methods for measuring roughness. Many researches are performed to estimate roughness by deploying smartphone sensors. However, no consideration is given to the influence of the speed of the host vehicle in roughness evaluation using smartphones. This work explains a smartphone sensor-based roughness evaluation technique by deploying the quarter car simulation model. The accuracy is checked with the simultaneously collected international roughness index (IRI) measured by a roughometer. Results of the smartphone-based pavement roughness estimation experiment showed a high correlation value of 0.73, and proved the accuracy of the method. The data were segregated based on three speed ranges. The correlation between the smartphone-based and roughometer-based IRI for all ranges was analyzed, and the R 2 value of 0.75 was exhibited for 31–50 km/h range.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".