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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".