Vehicular Broadcast Messaging Reliability Enhancement Protocol for Emergency Vehicle Communications
Bibliographic record
Abstract
This paper presents a new reliability enhancement to safety messaging system particularly useful for emergency vehicle (EV) communications. This enhancement makes meeting stringent quality of service (QoS) requirements particularly prevalent in safety applications of Dedicated Short Range Communications (DSRC). We show that emergency- Passive Cooperative Collision Warning (ePCCW) enhances EV performance in particular, and measurement of the effect on surrounding vehicles relative to the desired EVs is also shown. ePCCW protocol is an imperfect distributed protocol, and both analytic and simulation results agree and show a significant improvement in EV communication reliability. Substantial improvement in reliability or probability of success relative to a leading alternative is realized. Highway environment is simulated with mobility modeled using the well known Simulation of Urban MObility (SUMO), and the DSRC Physical layer (PHY) is simulated using an accurate Orthogonal Frequency Division Multiplexing (OFDM) PHY simulator, with varying channel conditions based on mobility model and highway environment. Additionally, the proposed system is shown to have a decreased average timeslots delay that is well within acceptable delay threshold, and provides the best reliability in its class.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".