A Survey on Artificial-Intelligence-Based Internet of Vehicles Utilizing Unmanned Aerial Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
As Autonomous Vehicles continue to advance and Intelligent Transportation Systems are implemented globally, vehicular ad hoc networks (VANETs) are increasingly becoming a part of the Internet, creating the Internet of Vehicles (IoV). In an IoV framework, vehicles communicate with each other, roadside units (RSUs), and the surrounding infrastructure, leveraging edge, fog, and cloud computing for diverse tasks. These networks must support dynamic vehicular mobility and meet strict Quality of Service (QoS) requirements, such as ultra-low latency and high throughput. Terrestrial wireless networks often fail to satisfy these needs, which has led to the integration of Unmanned Aerial Vehicles (UAVs) into IoV systems. UAV transceivers provide superior line-of-sight (LOS) connections with vehicles, offering better connectivity than ground-based RSUs and serving as mobile RSUs (mRSUs). UAVs improve IoV performance in several ways, but traditional optimization methods are inadequate for dynamic vehicular environments. As a result, recent studies have been incorporating Artificial Intelligence (AI) and Machine Learning (ML) algorithms into UAV-assisted IoV systems to enhance network performance, particularly in complex areas like resource allocation, routing, and mobility management. This survey paper reviews the latest AI/ML research in UAV-IoV networks, with a focus on resource and trajectory management and routing. It analyzes different AI techniques, their training features, and architectures from various studies; addresses the limitations of AI methods, including the demand for computational resources, availability of real-world data, and the complexity of AI models in UAV-IoV contexts; and considers future research directions in UAV-IoV.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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