Trailer Mass Estimation Using System Model-Based and Machine Learning Approaches
Why this work is in the frame
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Bibliographic record
Abstract
Trailer mass is one of the important trailer parameters that affects the stability of the tractor-trailer systems. In this paper, two different approaches are proposed to estimate trailer mass for arbitrary tractor-trailer configurations; dynamic system model-based and Machine Learning (ML) approaches. The stability of the dynamic system model-based estimation algorithm is analyzed, establishing the convergence of the estimation error to zero. In the proposed ML-based approach, a Deep Neural Network (DNN) is designed to estimate trailer mass. The inputs of the ML-based method have been selected based on the tractor-trailer dynamic model, and are considered to be normalized by the tractor mass, tire sizes, and geometry so that re-training of the network is not needed for different towing vehicles. The simulation and experimental results justify the accuracy of the trailer mass estimation in various cases and demonstrate that the trailer mass can be estimated with less than 10% error.
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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 | 0.000 |
| 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 it