Distributed relative cooperative positioning in Vehicular Ad-Hoc Networks
Bibliographic record
Abstract
We propose a distributed Relative Cooperative Positioning algorithm (ReCoP) that computes the position in relation to other vehicles in the network. ReCoP does not depend on fixed reference node/s or infrastructure, and it is independent of Global Navigation Satellite System (GNSS) reading. Instead one node in each group of one-hop connected neighbours establishes relative map for group members. Then when messages (e.g., warning messages) arrive to the vehicle with gateway responsibility, the coordinates transform to be recognized by different relative maps. This approach can be utilized in data dissemination to reduce broadcast storm problem due to redundant retransmissions. It has been compared with the Local Self Positioning (LSP) in which each vehicle individually builds its own local relative map. The performance of the proposed algorithm, ReCoP, has been evaluated with respect to different traffic density, transmission range, and speed. The simulation results illustrate that ReCoP computes the relative position with more precision than LSP. It also demonstrates the scalability, robustness, and flexibility of the proposed ReCoP compared to LSP.
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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 | 0.002 |
| 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 | 0.001 | 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 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".