Data-Efficient Vehicular Fuel Rate Estimation Via Transfer Learning Using a Nonlinear-Autoregressive-Exogenous Neural Network
Bibliographic record
Abstract
Fuel estimation is crucial for intelligent transportation systems to minimize fuel consumption, thereby reducing costs and environmental impacts. However, developing accurate data-driven fuel-estimation models requires extensive data, which is usually costly and time-consuming to collect for feet systems with a large number of vehicles. This paper presents an inductive transfer learning (TL) method for fuel rate estimation to alleviate the large fuel data measurement requirement. First, a dynamic neural network based on the Nonlinear Auto-Regressive Exogenous (NARX) approach is trained for a Peterbilt 579 class 8 haul truck as the base model, using on-road fuel consumption measured data. Afterward, using the TL method, the developed fuel-estimation model is leveraged to three different vehicles with various engine sizes and types, including a Ford F-350, a hybrid Ford Fusion, and a turbocharged Ford Escape. Extensive analyses illustrated that the transferred target models require training data as minimal as 3% (3km) of the total data used for training the base model to achieve prediction accuracy comparable to the base model. Moreover, compared to developed conventional models, the TL-based target models for the Ford Fusion, Ford Escape, and Ford F-350 demonstrate average improvements of 70%, 71%, and 73% in terms of Mean Absolute Error (MAE), Rooted Mean Squared Error (RMSE), and training time, respectively.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".