Cloud-Assisted Real-Time Road Condition Monitoring System for Vehicles
Bibliographic record
Abstract
Road infrastructure is the life line of the transportation industry and it should be monitored at regular intervals to ensure that it provides a smooth riding experience, safety to the passenger and causes less damage to the vehicles. Road conditions are affected by several factors such as weather conditions, accidents that have occurred, and regular wear and tear, hence it is difficult to monitor them in real-time. Previous research works on road monitoring systems can be broadly classified into three groups; the first group uses sensor data to detect road conditions based on a given threshold, the second employs a machine learning algorithm to acquire sensor data from the vehicle, while the third uses machine learning at the server then transmits the result back to the vehicle. The learning algorithms of groups two and three provide better results than group one. Group three yields the more accurate results, but at the cost of time. Therefore, in this paper, we propose a novel system that monitors road conditions in realtime by learning from the data obtained from built-in sensors of a smartphone that is mounted inside the vehicle. We have designed a lightweight learning algorithm that improves accuracy by interacting with the server and monitoring road conditions in real-time. The algorithm is based on k-means clustering and our experimental results show that it can classify road conditions based on the accelerometer data with 88.67% accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".