PROBABILISTIC-BASED CROWDSOURCING TECHNIQUE FOR ROAD SURFACE ANOMALY DETECTION
Bibliographic record
Abstract
Abstract. Road surface monitoring is a critical key factor to serve the purpose of road safety and driving comfort. Recently, many efforts have been made in developing approaches to detect road surface anomalies using smartphone sensors. However, detecting road surface anomalies from smartphone sensors face considerable number of challenges due to the various factors affecting detection rate. By aggregating data from a large number of users (i.e., concept of crowdsourcing), the accuracy of detection can be increased, and the potential false positive and false negative detection rates raised from every single source (i.e., user) can be detected and filtered. In this paper, a novel probabilistic-based crowdsourcing technique is proposed to classify and combine road surface anomalies (i.e., dynamic events) detected from various smartphones on-board vehicles. The proposed approach can integrate detected events from multiple users which are not an absolute binary scenario primarily caused by different sensing capabilities of various participators’ smartphone sensors and diversity in mechanical properties of vehicles. Furthermore, this approach considers the spatiotemporal behaviour of reported road surface anomalies from different users in different times and locations. The experimental results show that the proposed crowdsourcing method improves the accuracy and rate for detecting road surface anomalies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".