A Comprehensive Survey on Challenges and Issues in V2X and V2V Communication in 6G Future Generation Communication Models
Bibliographic record
Abstract
The latest generation of technologies for Information and Communication Technologies (ICT) with V2V that links multiple vehicle to each other and Vehicle to Everything (V2X) links vehicles to every other object in the world.In addition to making travel more pleasant and secure, it also has far-reaching implications for enhancing traffic efficiency, decreasing pollution, and lowering accident rates with better resource utilization levels.V2X applications raise concerns regarding traffic safety and data security that have not yet been thoroughly assessed, as the technology is still in its infancy.There needs to be extensive testing and verification of the technology before it can be released to the public.As the quantity of vehicles on the highway continues to rise, vehicular networks must contend with new difficulties, including volatility, diversity, and scale.Collective Perception (CP) refers to the sharing of sensor data in V2X communication.V2X-capable stations, such as autos, vulnerable road users, and Roadside Units (RSUs), can enhance traffic safety and efficiency in VANETs by exchanging lists of detected items inside the designated frequency band.This process also helps optimize resource consumption.Position, direction, and speed are just few of the qualities that describe an object for the sake of traffic safety.Additional stringent requirements for cellular-based vehicular networks include low latency, high reliability, and excellent spectrum efficiency with high life time.When the life time of connected autonomous vehicles finally arrives, there will be a plethora of innovative uses in a wide range of transportation settings and scenarios.A V2X and V2V communication networks that are both to make this great idea come true, we need computers that are very smart and can handle the very fast, very reliable, low-latency transfer of huge amounts of data.6G communication technologies are poised to meet the requirements of next-gen V2X.In this research, a variety of enabling technologies, such as novel data sources, algorithms, and system architectures for V2V, V2X data transmissions, securing the data and communicating with other vehicles and RSUs are discussed.This research is helpful for numerous research scholars, academicians and industry experts for getting complete information of the working of available models and their limitations, so that innovative models can be designed with better efficiency.
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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 | 0.003 |
| 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".