A Review of Dynamic Wireless Charging and Reservations for CAEV and UAV in 5G/6G ITS
Bibliographic record
Abstract
An overview of the dynamic wireless charging (DWC) system design and architectures is presented to highlight its profound impact within the dynamic framework of 6G-enabled intelligent transportation systems (ITS). A historical overview of DWC architecture is provided to trace its evolution and highlight emerging trends. Thus, establishing a foundational understanding of this rapidly developing field. This paper also focuses on the management of charging requirements for both connected and autonomous electric vehicles (CAEVs) and unmanned aerial vehicles (UAVs). A survey of innovative charging reservation strategies that are vital for optimal use of DWC infrastructure is presented. Further, the effect of early and late-arriving vehicles on DWC reservation systems is explored to highlight areas of improvement for research. Finally, we propose our meticulously designed architecture for the DWC reservation and trip planning for efficient charging of both CAEVs and UAVs. This is concluded by the presentation of innovative strategies to manage early and late arrival scenarios of both CAEVs and UAVs. Thus, promoting a more sustainable, efficient, and interconnected future of CAEV and UAV charging within the 5G/6G ITS.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".