Anomalous Behaviour Detection via Event-Based Metric with Sequential Tracking in a V2X Environment
Bibliographic record
Abstract
Massive amount of data transmission in vehicle-to-everything (V2X) settings lead to heavy utilization of communication channels. Furthermore, reliable connectivity and fast data transmission can be achieved by either re-engineering the network architecture or using efficient methods that alter data attributes, such as volume. This paper proposes a new method called Sequential Tracking along with an event-based distracted driving detection algorithm. Existing studies using machine learning models and object detection aim to detect distracted drivers and send their detection outcomes to the cloud or edge units. However, minimizing the data transmission or exchange overhead remains understudied. Therefore, the proposed Sequential Tracking method with an event-based algorithm is applied to distracted driving detection models to reduce the data transmission overhead due to false predictions, i.e., false positives or false negatives. Furthermore, the proposed method considers the camera's inference time and storage capacity since AI models are deployed to edge units for these kinds of tasks. Numerical results, with the inclusion of parameter tuning, confirm that the overall accuracy performance of the model can be improved from 87% to 91%. Moreover, following upon parameter-tuning, false predictions in the test dataset are eliminated, and the number of data points is reduced to less than one-tenth leading to significant traffic reduction between the edge unit and the cloud.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".