Sustainable and Ethical Edge AI in Autonomous Vehicle Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it