Estimation of Vehicle–Trailer Hitch-Forces and Lateral Tire Forces Independent of Trailer Type and Geometry
Bibliographic record
Abstract
Abstract In this paper, a new approach in estimating the lateral tire forces and hitch-forces of a vehicle–trailer system is introduced. It is shown that the proposed hitch-force estimation is independent of trailer mass and geometry, by utilizing the vehicle velocity, acceleration, torque engine, wheel's speed, and steering angle measurements. The designed lateral tire forces and hitch-force estimations' algorithm can be used for any ball type trailer without any priori information on the trailer parameters. A vehicle–trailer dynamic model is proposed to design an observer for the estimation of the hitch-forces and lateral tire forces. Simulations' studies in carsim along with experimental tests are used to validate the presented method. The results confirm the accuracy of the developed observer, and the experimental tests' results show that there is a good agreement between the estimated and actual lateral tire forces as well as the hitch-forces.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".