Sustainable and Ethical Edge AI in Autonomous Vehicle Networks
Bibliographic record
Abstract
The development of autonomous vehicles (AVs) has brought about a new era of smart transportation. These selfdriving cars use advanced software to understand their surroundings and make decisions, which helps create a smarter way of moving people and goods. To make safe and quick decisions, AVs need strong data processing with little delay. However, traditional cloud computing systems have problems like slow response times, high energy use, and privacy issues. This delay can be very dangerous when quick decisions are needed on the road. Moreover, cloud systems use a lot of energy and may raise privacy concerns because all data must be sent to outside servers. Edge Artificial Intelligence (Edge AI) offers a better solution by processing information close to the source, either directly on the vehicle or on nearby roadside units, instead of relying on far-off cloud servers. This paper looks at how sustainable and ethical Edge AI can be used in autonomous vehicle networks. It concludes that combining eco-friendly computing with responsible AI practices can help build a smarter, safer, and more trustworthy autonomous driving system
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".