Evaluation of Redundancy Mitigation Rules in V2X Networks for Enhanced Collective Perception Services
Bibliographic record
Abstract
Collective perception enables connected vehicles to share detailed environmental data, significantly enhancing situational awareness and safety. This data sharing is crucial for the functioning of modern vehicular networks, but it introduces the challenge of managing redundant information, which can congest communication channels and degrade network performance. To address this challenge, several redundancy mitigation rules have been proposed and extensively evaluated to filter out unnecessary data. This work investigates the impact of different redundancy mitigation rules on the performance of connected vehicular networks with collective perception under different market penetration rates. Additionally, the study introduces a set of hybrid rules designed to optimize this balance for collective perception services in vehicular networks. These hybrid rules are compared to scenarios without object filtering and other existing redundancy mitigation rules. Key performance metrics include channel busy ratio, environment awareness ratio, redundancy level, and the age of information. By analyzing the metrics as a function of the distance between the reported object and the receiving connected vehicle, the study identifies key trends in balancing redundancy reduction with information freshness under diverse network conditions. The results demonstrate that hybrid redundancy mitigation rules outperform existing approaches by effectively balancing channel load, redundancy level, and environment awareness, while maintaining lower age of information values. This balance is particularly crucial for safety-critical objects in close proximity to the connected vehicle. The findings highlight the importance of intelligent redundancy mitigation strategies in enhancing the timeliness and reliability of information in densely populated vehicular networks, ensuring the efficient and safe operation of connected vehicles.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".