A Joint Design of Platoon Communication and Control Based on LTE-V2V
Bibliographic record
Abstract
Recently, vehicle platooning has attracted a lot of attention as one of the promising solutions to the explosion of vehicle numbers. By exchanging information among vehicles through communication networks, vehicle platooning can significantly improve traffic safety and efficiency. In this paper, we develop a joint systematic design of platoon communication and control to reduce position errors of consecutive vehicles and to improve platoon safety. Through separate information dissemination of the platoon leader and followers, we improve the success probability of the platoon leader's information dissemination. To extend the communication range of the platoon leader and also the platoon scale, we propose to use relays to forward the platoon leader's messages. Further, an adaptive distributed model predictive control (DMPC) based controller is presented, which can adjust its control parameters according to platoon state and dynamic information sources. Simulation results not only verify the proposed systematic design in terms of position errors, but also show that our scheme performs well in vehicle failure cases where collisions can be avoided and platoon safety improved.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".