A quantitative analysis of improved DSRC system using repetition based broadcast safety messaging with hidden terminals
Bibliographic record
Abstract
Broadcast communications are widely proposed for safety messaging. In the case of highway vehicular networks however, constantly broadcasting safety messages inevitably cause the well-known hidden terminal problem. Three existing repetition-based broadcasting protocols have shown to meet the reliability and delay requirements for DSRC safety systems. However, when compared with one another, all of these protocols have not been tested under hidden terminals scenario. In this paper, we propose a quantitative model to evaluate the quality of service (QoS) of DSRC systems using these three leading repetition-based protocols under hidden terminals and highway scenarios. The performance of our model is analyzed by means of probability of success and delay performances. Our performance study shows that the repetition-based protocols do not meet the DSRC critical safety reliability requirements when tested under higher transmission loads with hidden terminals taken into account. Under lower transmission loads, these protocols only meet the reliability requirements at relatively low vehicle densities. Our quantitative model and performance results presented in this study also serve as a benchmark for further improvement of DSRC systems' performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".