A Distributed Reference Governor Approach to Ecological Cooperative Adaptive Cruise Control
Bibliographic record
Abstract
To achieve a safe vehicle platooning system, it is essential to guarantee the string stability of the vehicle platoon while handling the safety, comfort, and performance constraints. This paper presents a distributed reference governor (RG) approach to the constraint handling of vehicle platoons equipped with cooperative adaptive cruise control. First, a string stable platoon is designed based on a frequency-domain approach. Second, an RG is designed that sits behind the controlled system and keeps the output inside the defined constraints. RG does not change the behavior of the controlled system; therefore, the platoon remains string stable based on the frequency-domain design. Only when there is a possibility of violating the defined constraints, the RG would intervene to push the system back into the constraints. Third, to improve the platoon's energy economy, a controller is presented for the leader's control using nonlinear model predictive control method, assuming it is a plug-in hybrid electric vehicle. Evaluations performed with a platoon model constructed using high-fidelity models of the baseline vehicle show that the proposed method is able to simultaneously maintain the string stability and platooning constraints, while improving the total energy economy of the entire platoon. Moreover, the results of the hardware-in-the-loop testing demonstrate the performance of the proposed controller in real-time application.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".