About Deterministic and non-Deterministic Vehicular Communications over DSRC/802.11p
Bibliographic record
Abstract
In this work, we introduce a priority-aware deterministic access protocol called Vehicular Deterministic Access VDA. VDA is based on 802.11p/DSRC and allows vehicles to access the shared medium in collision-free periods. Particularly, VDA supports two types of safety services emergency and routine safety messages with different priorities and strict requirements on delay. To avoid long delays and high packet collisions, VDA allows vehicles to access the wireless medium at selected times with a lower contention than would otherwise be possible within a two-hop neighborhood by the classical 802.11p Enhanced Distributed Channel Access or Distributed Coordination Function schemes. A non-VDA-enabled vehicle, that is, a vehicle not configured with the optional VDA capability over 802.11p, may start transmitting on the shared channel just before or during the VDA opportunities reserved for vehicles with VDA capabilities. To avoid the aforementioned issues and prevent interfering transmissions from VDA-enabled vehicles and non-VDA-enabled vehicles, we also proposed a novel scheme called extended VDA. We analyzed the impact of several design tradeoffs between the contention free period/contention period dwell time ratios on the performance of safety applications with different priorities for VDA and extended VDA. Simulations show that the proposed schemes clearly outperform the backoff-based schemes currently used by 802.11p in high communication density conditions while bounding the transmission delay of safety messages and increasing the packet reception rate. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".