Interoperability between Deterministic and Non-Deterministic Vehicular Communications over DSRC/802.11p
Bibliographic record
Abstract
In recent works, a priority-aware deterministic access protocol that is based on 802.11p/DSRC was introduced to allow vehicles to access the shared medium in collision-free periods. The VANET Deterministic Access (VDA) protocol as introduced in [8] has no mechanism that prevents a non VDA-enabled vehicle from accessing the channel in a scheduled VDA opportunity (VDAOP). A non VDA-enabled vehicle, i.e. a vehicle not configured with the optional VDA capability over 802.11p, may start transmitting on the shared channel just before or during the VDAOPs reserved for vehicles with VDA capabilities. Also, non VDA-enabled vehicles may be prevented from accessing the shared channel due to the transmission of VDA-enabled vehicles during their respective VDAOPs with a higher priority (shorter AIFS). In this work, we propose a new enhanced VDA scheme, called EVDA that avoids the above issues and prevents interfering transmissions from VDA-enabled vehicles and non VDA-enabled vehicles. We also analyzed the impact of several design tradeoffs between the Contention Free Period (CFP)/Contention Period (CP) Dwell-time ratios on the performance of safety applications with different priorities with EVDA. Simulations show that the proposed scheme clearly outperforms the VDA scheme in high communications density conditions while bounding the transmission delay of safety messages and increasing the packet reception rate.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".