A Separation Strategy for Connected and Automated Vehicles: Utilizing Traffic Light Information for Reducing Idling at Red Lights and Improving Fuel Economy
Bibliographic record
Abstract
Vehicle platoon composed of a group of connected and automated vehicles (CAVs), a coordinated movement strategy, has been widely proposed to address a range of traffic problems. The motion of vehicle in the platoon passing signalized intersections can significantly affect their total trip time and fuel consumption. With the development of advanced communication technology such as V2V and V2I, CAVs can automatically obtain and use the upcoming traffic light timing information to find optimal velocity profiles that can avoid idling at red lights. This paper proposes an optimal velocity control and separation strategy for the platoon to minimize the trip time and reduce fuel consumption as much as possible. Simulation results show that with the introduction of the velocity control and separation strategy, the total trip time and fuel consumption decrease by 19.2% and 18.1%, respectively. Thus the effectiveness of the proposed strategy is demonstrated.
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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 | 0.001 |
| 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".