Cooperative target tracking in vehicular sensor networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Target tracking plays a key role for vehicular ad hoc networks (VANETs) due to the fact that a wide variety of envisioned applications rely on the ability of this technique of detecting, localizing, and tracking objects surrounding a vehicle. This subject has been studied in fields such as airborne traffic, computer vision, and wireless sensor networks. A VANET brings out new challenges that should be addressed. For instance, the cluttered and dense scenarios, communication issues such as short term links, and the variety of objects considered to be targets, are some of the new ingredients to be taken into account. Applications such as collision warning/avoidance systems require strict time constrains, while others impose only mild restrictions. This complex and heterogeneous environment is discussed in this work, where we didactically divide the main problems into four components: the targets’ motion model, measurement models, data association problem, and filtering. We also discuss the communication issues and how they affect these systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it