Classification and speed estimation of vehicles via tire detection using single‐element piezoelectric sensor
Bibliographic record
Abstract
Summary This paper presents novel vehicle classification technology by utilizing a single‐element piezoelectric sensor placed diagonally on a traffic lane to accurately identify vehicles. Novelty of this technique originates from using diagonally placed piezoelectric strip sensor and machine learning technology to provide a highly accurate and cost‐effective alternative to current vehicle classification systems. Diagonal placements of the piezoelectric strip sensor ensure detection of passing vehicle tires by facilitating vehicle classification process. Presented technology is capable of accurately classifying vehicles into a relatively large number of classifications, including motorcycle, which has proven to be a challenging category in present‐day commercial vehicle classifiers. Vehicle classification is a vital intelligent transportation systems application. Accurate data reporting aids suitable roadway design for safety and capacity and can also support other purposes, such as reporting highway congestion to the general public or providing area denseness data to interested businesses. To make a classification decision, a vehicle's signal is acquired from diagonal piezoelectric strip sensor, processed, and then applied to a machine learning algorithm. A speed estimation technique using the same single‐element piezoelectric sensor was also developed, tested, and compared with an embedded vehicle classifier currently used by the Oklahoma Department of Transportation. Testing on several highway sites indicated up to 97% classification accuracy. This paper presents a complete description of the developed system, including sensor installation, data acquisition and processing, and classification algorithm. Overall, the system offers a high‐performance cost‐effective solution for vehicle classification that minimizes roadwork typically required for loop and sensor installations of current systems. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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".