Implementation of an Extended Model for Multi-Axle Articulated Vehicle with Nonlinear Tire Model
Bibliographic record
Abstract
A new extended planar model for multi-axle articulated vehicle with nonlinear tire model is presented. This nonlinear multi-axle articulated vehicle model is specifically intended for improving the model performance in operating regimes where tire lateral force is near the point of saturation, and it has the potential to extend the specific axles model to any representative configuration of articulated vehicle model. At the same time, the extended nonlinear vehicle model can reduce the model's sensitivity to the tire cornering coefficients. Firstly, a nonlinear tire model is used in conjunction with the 6-axle planar articulated vehicle model to extend the ranges of the original linear model into the nonlinear regimes of operation. Secondly, the performance analysis of proposed nonlinear vehicle model is verified through the double lane change maneuver on different road adhesion coefficients using TruckSim software. Simulation results demonstrate that the proposed vehicle model shows high accuracy, which provides accurate simulation of the vehicle states on different road adhesion coefficient.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".