Cooperative Perception: Mitigating Messages Content Duplication
Bibliographic record
Abstract
Autonomous Vehicles (AVs) and smart vehicles rely on cooperative perception to complement the information missed by their sensors due to their limited range and non-line of sight objects. There are many applications that rely on communication leaving sometimes limited resources for cooperative perceptions. Therefore, optimizing the resources and messages sent over the network becomes crucial. An important factor that undermines the potential of vehicular networks is the number of messages containing information about the same objects. Not only this issue exhausts the limited network bandwidth and may lead to dropping some messages in high-density traffics but also prevent other useful information from being shared. This paper suggests a Game Theory Based Transmission (GTBT) to mitigate redundant message content in a fog-based vehicular network. We show that this approach is real-time and achieves 10.5% fewer duplicate messages compared to the Max Score Based Transmission (MSBT) baseline while it does not require any additional communication costs. Moreover, we contrast the pros and cons of our approach against two other baselines: random (Rand) and geo-filtering (Geo) transmission-based approaches, showing that GTBT is able to minimize the lost information (5.3% for GTBT vs 15.2% and 18.3% for Geo and Rand respectively) and is competing with Geo approach in the number of unique messages sent and their value. Lastly, the GTBT is able to provide a gain of 1.7 as calculated by our suggested new metric as a measure of the ability to compensate for the missed information, vs (- 0.5) for the Geo approach. To the best of our knowledge, this is the first study that employs game theory for selecting the content of the message for the cooperative perception problem.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".