Live Traffic Video Multicasting Services in UAV-Assisted Intelligent Transport Systems: A Multiactor Attention Critic Approach
Bibliographic record
Abstract
Live traffic video is vitally important for vehicles in future intelligent transport systems (ITSs). Due to the limitation of onboard sensors, vehicles may not be able to obtain a full view of the traffic situations which endangers safety for autonomous driving vehicles. In this article, we propose a traffic video multicasting scheme by using video splitting and group splitting techniques for unmanned aerial vehicles (UAVs)-assisted ITS, in which UAVs are considered as the eyes in the sky to capture real-time traffic videos. We aim to maximize the long-term video quality received by vehicles by jointly optimizing vehicle grouping and spectrum allocation. Considering the interactions among UAVs, the above optimization problem is formulated as a multiagent coordination problem in the form of a Markov game (MG). The MG is subsequently solved by leveraging a state-of-the-art multiagent deep reinforcement learning (MADRL) algorithm, namely, multiactor attention critic (MAAC), in which an attention mechanism is utilized to pay attention to other agents to make the learning process more effective and scalable. Extensive simulation results show that the MAAC-based algorithm has better performance in terms of video quality and spectrum efficiency compared with the baseline methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".