Tracking an on the run vehicle in a metropolitan VANET
Why this work is in the frame
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Bibliographic record
Abstract
The Vehicular Ad Hoc Network (VANET) holds promises for on-road security applications. In this paper, we utilize the VANET for surveillance purpose, tracking a noncooperative mobile target. We explore the possibilities of engaging Onboard Units (OBUs) and Roadside Units (RSUs) in a metropolitan VANET for tracking a vehicle that is on the run. The uncertainty associated with the unplanned locomotion of a vehicle in the metropolitan road network, that exhibits dynamic characteristics, such as different speed limits and time varying traffic congestion, makes vehicle tracking challenging. We present a tracking system composed of three operational modules: localization, tracking data collection and prediction of future locations of a target. Tracking messages are communicated among the OBUs and RSUs and are triggered on in probable areas where the target may be present. Therefore, another imperative element of the addressed problem is to scope the search to limit the number of OBUs and RSUs involved in the tracking operation. Our proposal does not presume any motion model for the target. A novel movement modeling technique utilizes OBU observations to classify the target's movement pattern. We propose a Dirichlet-multinomial model under the Bayesian estimation framework. The movement estimation is then exploited for predicting future locations of the target. The proposed method is analogous to chasing an on the run vehicle using police squad cars. We believe this approach holds potentials as an alternative to high-speed pursuits.
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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 | 0.001 | 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 | 0.001 | 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 it