Performance of Radio Access Technologies for Next Generation V2VRU Networks
Bibliographic record
Abstract
The number of road accidents has remained stable in recent years. By using the latest technologies such as vehicle-to-vehicle communications, it is possible to improve road safety and reduce the number of road fatalities, especially for vulnerable road users (VRUs). There are two existing radio access technologies (RAT) for vehicle-to-everything (V2X) communications, i.e., Wi-Fi -based by IEEE (802.11p and its next-generation standard 802.11bd), and cellular-based by 3GPP (LTE-V2X and 5G NR-V2X). Although many works have evaluated and compared the performance of V2V RAT communications, very little work has been done to compare the performance of these technologies in the context of vehicle-VRU communications. In this paper, we present, to the best of our knowledge, the first work that evaluates the performance of each RAT in the context of vehicle-to-pedestrian (V2P) and vehicle-to-cyclist (V2C) communications. Using four performance metrics, namely packet error rate (PER), packet reception rate (PRR), throughput, and latency, we examined whether each RAT can meet the requirements of safety applications intended for implementation in urban areas. The answer to this question is yes. However, each RAT has its own performance profile. In terms of PER and PRR, 802.11bd has an advantage, while in terms of throughput and latency, 5G NR-V2X performs better.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".