An Eco-Driving Advisory System for Continuous Signalized Intersections by Vehicular Ad Hoc Network
Bibliographic record
Abstract
With the vehicular ad hoc network (VANET) technology which support vehicle-to-vehicle (V2V) and vehicle to road side unit (V2R/R2V) communications, vehicles can preview the intersection signal plan such as signal countdown message. In this paper, an ecodriving advisory system (EDAS) is proposed to reduce CO2 emissions and energy consumption by letting the vehicle continuously pass through multiple intersections with the minimum possibilities of stops. We extend the isolated intersection model to multiple continuous intersections scenario. A hybrid method combining three strategies including maximized throughput model (MTM), smooth speed model (SSM), and minimized acceleration and deceleration (MinADM) is designed, and it is compared with related works maximized throughput model (MaxTM), open traffic light control model (OTLCM), and predictive cruise control (PCC) models. Some issues for the practical application including safe car following, queue clearing, and gliding mode are discussed and conquered. Simulation results show that the proposed model outperforms OTLCM 25.1%~81.2% in the isolated intersection scenario for the CO2 emissions and 20.5%~84.3% in averaged travel time. It also performs better than the compared PCC model in CO2 emissions (19.9%~31.2%) as well as travel time (24.5%~35.9%) in the multiple intersections scenario.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".