Intelligent DWC Reservation and Trip Planning of CAEV and UAV for 6G ITS
Bibliographic record
Abstract
In the 6G era, addressing charging challenges for electric vehicles is paramount. This paper focuses on mitigating concerns for both Connected Autonomous Electric Vehicles (CAEVs) and Unmanned Aerial Vehicles (UAVs). CAEV users face range anxiety, while UAV operators contend with limited battery sizes. The paper introduces a dynamic wireless charging (DWC) reservation and trip planning system, powered by 6G, vital for future smart cities. It provides a literature review on CAEV and UAV charge scheduling, proposing a novel system design tailored for 6G-enabled Intelligent Transportation Systems (ITS), where UAVs serve CAEVs. Notably, for vehicle-to-vehicle DWC and communication, laser technology is utilized, ensuring reliability and efficiency. A dynamic arrival handling protocol addresses unpredictable vehicle arrivals. Simulation results showcase the system’s efficiency, optimizing charging and reducing wait times. With the impending 6G era, the paper aims to advance charging infrastructure for intelligent, flexible, and harmonized electric vehicle charging operations.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".