Edge Network-Assisted Real-Time Object Detection Framework for\n Autonomous Driving
Bibliographic record
Abstract
Autonomous vehicles (AVs) can achieve the desired results within a short\nduration by offloading tasks even requiring high computational power (e.g.,\nobject detection (OD)) to edge clouds. However, although edge clouds are\nexploited, real-time OD cannot always be guaranteed due to dynamic channel\nquality. To mitigate this problem, we propose an edge network-assisted\nreal-time OD framework~(EODF). In an EODF, AVs extract the region of\ninterests~(RoIs) of the captured image when the channel quality is not\nsufficiently good for supporting real-time OD. Then, AVs compress the image\ndata on the basis of the RoIs and transmit the compressed one to the edge\ncloud. In so doing, real-time OD can be achieved owing to the reduced\ntransmission latency. To verify the feasibility of our framework, we evaluate\nthe probability that the results of OD are not received within the inter-frame\nduration (i.e., outage probability) and their accuracy. From the evaluation, we\ndemonstrate that the proposed EODF provides the results to AVs in real-time and\nachieves satisfactory accuracy.\n
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".