Age of Processing-Aware Offloading Decision for Autonomous Vehicles in 5G Open RAN Environment
Bibliographic record
Abstract
In state-of-the-art autonomous vehicles, data from the vehicle's sensors is often processed using fast and expensive onboard hardware. Such an onboard processing scheme quickly drains the vehicle's battery and consumes computing resources. Recent research proposed to offload parts of processing tasks onto cloud. However, offloading tasks to the cloud is challenging because of the low latency needed for reliable and safe autonomous driving decisions. To address this issue, we propose an Age of Processing (AoP)-aware offloading mechanism for autonomous vehicles. First, we develop a collaboration space of edge clouds to process data closely as possible to the vehicles. Second, we reveal a new communication planning model that allows the vehicle to find suitable open radio units available in route to offload tasks to edge clouds and reduce variation in offloading delay. Third, we formulate an optimization problem that minimizes AoP, i.e., elapsed time from generating tasks and getting computation results. Our AoP-based approach allows a status update to be available to the vehicle after computation. To solve the formulated non-convex problem, we apply dual decomposition and design an AoP-aware algorithm to compute the solution in near real-time. The results demonstrate that our approach meets computation deadlines while minimizing AoP.
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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 | 0.001 | 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".