Ecological NMPC Controller for Connected and Automated Plug-in Hybrid Electric Vehicles at Roundabouts.
Bibliographic record
Abstract
Connectivity and automation provide a spectacular opportunity to improve traffic flow, safety, and efficiency due to their capability to exchange critical driving data and information. On the other hand, plug-in hybrid electric vehicles (PHEVs) have been a topic of extensive research in the past and it was demonstrated that it is a good solution for improving fuel economy and ecological features and their performance enhances with the addition of connectivity and automation. Coordination of vehicles at an urban area like roundabouts is a demanding problem especially by knowing that chance of both lateral and longitudinal collision exists. This paper proposes an approach for solving nonlinear multi-objective optimal control problem of decentralized coordination of connected and automated PHEVs (CA-PHEVs) at roundabouts with consideration of fuel economy. It was found that the proposed controller can calculate priority based on a navigation function and provide a safe gap between vehicles. Also, addition of energy cost to the performance index can improve the fuel consumption of the vehicle.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".