Hybrid Distance-Based Redundancy Mitigation Mechanisms for Collective Perception in Connected and Automated Driving
Bibliographic record
Abstract
This paper presents an extension to ETSI redundancy mitigation rules (RMRs) aimed at improving the efficiency of collective perception services in vehicular networks. The proposed mechanisms integrate the ETSI RMRs with distance-based criteria to exclude redundant locally perceived objects from collective perception messages (CPMs). The study evaluates the performance of several strategies: no RMR, self-announcement-based (SAB) RMR, the proposed distance-based SAB (DSAB) RMR, frequency-based (FB) RMR, and the proposed distance-based FB (DFB) RMR, across various market penetration rates. Key metrics analyzed include redundancy level (RL), channel busy ratio (CBR), and environment awareness ratio (EAR). The findings demonstrate that the distance-based extension consistently outperforms traditional methods in balancing redundancy, channel load, and environmental awareness, particularly at higher market penetration rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".