Research on Performance and Function Testing of V2X in a Closed Test Field
Bibliographic record
Abstract
The V2X and cooperative vehicle infrastructure system (CVIS), which leverage the efficient information interactions through V2V, V2I, V2P, and V2N, are known as the advanced and effective technology in reducing traffic accidents and improving traffic efficiency. The complex technical characteristics of V2X and highly reliable service demand of typical V2X applications call for the test needs before the large-scale deployment of V2X. It indicates that the performance and function of V2X devices should be systematically tested and evaluated in extreme and boundary conditions of driving and communication environments before being broadly deployed and applied in infrastructures. Motivated by the previously mentioned needs, a performance and function testing scheme of V2X in a closed test field is studied. According to the analytical viewpoint from the physical layer and MAC layer, the proposed research systematically analyses the technical differences of DSRC and LTE-V, which are two typical V2X protocols, in terms of vehicle speed, communication distance, and channel adaptability. Based on the critical practical test needs from the analytical study, a function and performance test system of V2X specifically for the closed test field is proposed. The performance and typical application effectiveness in intersection environment of DSRC and LTE-V are evaluated. The limitation and proposed improvement strategies of these V2X protocols are analytically discussed.
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 | 0.003 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".