A Packet Collision Avoidance Resource Selection Scheme for Reliable Intra-Platoon Message Delivery in a C-V2X Network
Bibliographic record
Abstract
Reliable intra-platoon communication is critical for safety-related message delivery within a platoon of connected and automated vehicles. However, the intra-platoon communication is challenged by packet collisions due to hidden nodes and merging collisions due to vehicle mobility. To address these challenges, this paper proposes a packet collision avoidance resource selection (PCA-RS) scheme to enhance the standardized SPS scheme. The proposed PCA-RS scheme introduces three enhancement mechanisms, which aims to alleviate merging collisions and hidden-node collisions in intra-platoon message delivery. A resource partition mechanism is introduced to divide frequency-time resources in a selection window into two sets in order for vehicles (both platoon and non-platoon vehicles) moving in opposite directions to select different frequency-time resources and thus avoid potential merging collisions; an intra-platoon cooperative mechanism is introduced to enable the leader of a platoon to know the resource occupation status on the hidden nodes of the platoon according to the messages received from the last platoon member of the same platoon and thus avoid potential hidden-node collisions; and a merging collision detection mechanism is introduced to enable a non-platoon vehicle to detect the status of the frequency-time resources it currently occupies after the non-platoon vehicle changes a lane and thus avoids potential merging collisions among non-platoon vehicles due to lane-change maneuvers. Simulation results demonstrate that compared with the standardized SPS scheme, the proposed PCA-RS scheme can improve the reliability of intra-platoon message delivery in terms of the intra-platoon packet delivery ratio.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".