How Close are We to Realizing a Pragmatic VANET Solution? A Meta-Survey
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
Vehicular Ad-hoc Networks (VANETs) are seen as the key enabling technology of Intelligent Transportation Systems (ITS). In addition to safety, VANETs also provide a cost-effective platform for numerous comfort and entertainment applications. A pragmatic solution of VANETs requires synergistic efforts in multidisciplinary areas of communication standards, routings, security and trust. Furthermore, a realistic VANET simulator is required for performance evaluation. There have been many research efforts in these areas, and consequently, a number of surveys have been published on various aspects. In this article, we first explain the key characteristics of VANETs, then provide a meta-survey of research works. We take a tutorial approach to introducing VANETs and gradually discuss intricate details. Extensive listings of existing surveys and research projects have been provided to assess development efforts. The article is useful for researchers to look at the big picture and channel their efforts in an effective way.
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 | 0.016 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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