Heterogeneous Vehicular Platooning with Stable Decentralized Linear Feedback Control
Bibliographic record
Abstract
Platooning which is defined as controlling a group of autonomous vehicles (multiple followers and one leader) to have a desired distance between them while following a desired trajectory has caught on recently in the control engineering discipline. Platooning brings along promising advantages, namely, increasing highway capacity and safety, and reducing fuel consumption. In this paper, using linearized longitudinal dynamic models for each vehicle, we investigate the control problem of vehicular platooning to have all vehicles followed the leader under a constant spacing policy. Under decentralized linear feedback controllers and taking account of heterogeneity in the dynamic models and feedback information to the vehicles, a general dynamic representation for the platoon is obtained. Having this and the proposed controller, stability analysis is developed for any information flow topology (IFT) between vehicles and any number of vehicles. As a case study, a platoon with one leader and two followers is investigated through the proposed strategy, and its stability conditions are provided. Numerical simulations are provided in which the stability range of control gains and the effect of different FTs on the performance of the platoon are discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".