An integrated vehicle velocity and tyre-road friction estimation based on a half-car model
Bibliographic record
Abstract
Knowledge of tyre-road friction force is important in many vehicle control systems such as torque vectoring, differential braking and stability control systems. However, tyre friction forces and the factors affecting the friction forces cannot be directly measured by commonly used vehicle sensors. Therefore, an accurate estimation of the friction forces and friction index is crucial for a successful vehicle control design. Although a large number of friction estimation algorithms have been developed, those algorithms mainly focus on a single direction, either longitudinal or lateral, but cannot estimate the tyre friction in a combined condition. This paper presents an integrated friction estimation algorithm based on a half-car vehicle model that can simultaneously estimate the combined friction condition along the longitudinal and lateral directions with some basic measurements. The algorithm consists of a number of estimations for vehicle roll angle, tyre friction forces, vehicle longitudinal and lateral velocities, and tyre-ground friction index. The proposed algorithm has been verified with actual vehicle test results. The test results demonstrate that the algorithm has a fairly good fidelity for estimation of tyre friction forces, vehicle velocities and road-friction conditions that is described quantitatively by a tyre-road friction index.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".