Machine learning-based estimation of vehicular emissions using on-board diagnostics data for intelligent fleet management
Bibliographic record
Abstract
Stringent regulations on real-driving emissions have been introduced to reduce the effect of tailpipe vehicular emissions on environmental pollution. The need to monitor emissions in real-driving conditions and across different driving cycles has underscored the importance of models for estimating emission rates. In this study, machine learning is employed to model commonly regulated tailpipe emissions (CO, UHC, NOx) based on real-time data obtained through on-board diagnostics (OBD) of vehicles. The models are trained using real-world tailpipe emission data and engine/vehicle operation data collected from three vehicles with various powertrains, including conventional gasoline engine, hybrid electric, and plug-in hybrid electric, under different ambient temperatures. Emphasis is placed on developing models capable of effectively estimating emissions during the cold phase of operation, which accounts for a significant portion of vehicular emissions, particularly in cold climates. The models are subsequently integrated into an intelligent fleet management system to enable real-time estimation of emissions using OBD data received from Internet of Things (IoT) modules installed on fleet vehicles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".