Performace Evaluation of Vehicular Communication
Bibliographic record
Abstract
Abstract Modern societies are built on good road infra-structure and efficient transport system. Safety is a high-priority consideration in development of road traffic systems. In recent years the weather information has become becomes very vital for road traffic safety because slippery roads are the key source of road accidents in northern regions of Europe, America and Canada as well. In this article we are presenting the test experiences and pilot road weather related services by executing a set of Vehicle to Infrastructure (V2I) communication scenarios by using IEEE 802.11p and 5G test networks. We have made an effort to evaluate the performance of IEEE 802.11p and 3GPP (3rd Generation Partnership Project) 5G test network. We also analyzed the performance of IEEE 802.11p with and without safety feature for secure and reliable vehicular communication. The combination of IEEE 802.11p with 5G test network cellular network makes the traffic system heterogeneous for traffic safety. This heterogeneous system provides the opportunity to exploit the vehicle-based actuators, sensor, and observation data in order to produce the intelligent service platform and up-to-date real time services for vehicles. In this article we have also made a comparison by using an IEEE 802.11p system having safety feature of SafeCOP (Safe Co-operating Cyber-Physical Systems using Wireless Communication) project. SafeCOP is a European project that aims cyber-physical systems-of-systems relying on wireless communication for safe and secure cooperation. This safety feature will help to decrease the amount of road accidents (Car crashes, injuries and fatalities) by offering safe and secure V2V and V2I co-operation. The fundamental advantage of this kind of performance analysis is that the communication between Vehicle-to-Road Weather station (V2RWS) can be exchanged safely and reliably, at the cost of network resources consumed by a safety feature in IEEE 802.11p. It’s clearly presented in this paper, that the use of heterogeneous network and SafeCOP feature for vehicular networking has a clear potential in near future for vehicle’s safety and security of vehicular network.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".