A cloud-based eco-driving solution for autonomous hybrid electric bus rapid transit in cooperative vehicle-infrastructure systems: A dynamic programming approach
Bibliographic record
Abstract
—Efficient public transportation has always intrigued extensive research. Aiming to improve the commuting efficiency and fuel economy of the autonomous hybrid electric buses in the Bus Rapid Transit (BRT), a cloud-based eco-driving solution adopting dynamic programming and model predictive control is proposed in this paper. This solution contains an upper-level cloud-based scheduling strategy and a lower-level onboard predictive energy management, which is conceived to function in a Cyber-physical system of the cooperative vehicle-infrastructure system. The scheduling model carefully considered coupled spatiotemporal constraints for the driving of autonomous BRT buses, including traffic lights, traffic regulations, stations, and ride comfort. The onboard energy management leverages the pre-planned scheduling information to achieve near-optimal fuel economy. The eco-driving solution is examined in three scenarios with intersections, stations, and ramps. Simulation results show that the proposed method can deal with different spatiotemporal limits along the route, with virtually no non-essential stops and sudden acceleration or braking, and achieves 97%∼98% energy-saving potential compared with the baseline performance.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".