Guidelines for the Design of Vehicular Cloud Infrastructures for Connected Autonomous Vehicles
Bibliographic record
Abstract
Initiatives all around the world, funded by industries and governments, have devoted tremendous efforts to develop connected and autonomous vehicles (CAVs). Nowadays, different CAV prototypes are being tested on the roads. The natural next step in the evolution of this area will be the provisioning of infotainment applications for enjoyable commutes and trips. In this article, we discuss the motivation for vehicular clouds for content delivery aimed to support applications for CAVs. First, we review the main concepts of vehicular clouds. Consequently, we discuss the needs of vehicular clouds to support content distribution for applications in CAV scenarios. In addition, we highlight fundamental challenges regarding vehicular cloud that need to be tackled: self-organization, service discovery and management, and resource discovery and allocation. By doing so, we analyze the current works in the literature and portray their limitations. Moreover, we provide some guidelines to deal with the challenges of vehicular clouds for CAV applications. Finally, we present future research directions that might be considered for the design of large-scale vehicular cloud infrastructures for CAV applications.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".