Towards the Vehicular Metaverse: Exploring Distributed Inference With Transformer-Based Diffusion Model
Bibliographic record
Abstract
Generative artificial intelligence (GAI) is emerging as a promising solution for the vehicular metaverse due to its adaptable, high-quality, and multi-modal content generation capabilities. Particularly noteworthy is the recent introduction of the Sora model, a Transformer-based diffusion model, which exhibits exceptional performance in visual scenarios. However, diffusion vision transformer (DViT) models face limitations in terms of device resources, inference latency, and personalized requirements at the edge, despite their practical effectiveness in clouds. In response, we propose a DViT-enabled system to enhance vehicular metaverse services. Our approach involves a distributed DViT inference mechanism where road-side units (RSUs) and vehicles collaborate to execute the diffusion process and generate personalized content within vehicles using local prompts. Additionally, we address users' latency-sensitive service demands by formulating a distributed latency optimization problem that considers bandwidth, computation power, and dynamic positioning of heterogeneous devices. We then propose a value iteration-based distributed inference algorithm capable of adaptively determining optimal inference strategies within resource-constrained vehicular networks. Numerical simulations demonstrate that our approach achieves superior performance in reducing latency and enhancing success rates for inference tasks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".