Robust Estimation and Experimental Evaluation of Longitudinal Friction Forces in Ground Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
A longitudinal force estimation based on wheel dynamics and unscented Kalman filter is proposed in this report to address the difficulties in the conventional tire-based approaches. Although it seems that implementation of a tire model in the estimation procedure should result in more accurate results, especially for non-linear regions, complexities in identifying the tire parameters due to the variation of the road and tire conditions leads to inaccurate results for harsh maneuvers on slippery roads. Moreover, the estimation process requires reliable measurements and this necessitates utilizing dynamic models with feasible measurements. Consequently, wheel dynamics is employed to extend the fidelity of the algorithm. For such a model, wheel speeds as reliable and feasible measurements are available. In this strategy, the complex tire-road interaction can be discarded since the wheel speeds are being observed by wheel sensors and the values of the effective torques are provided by motor drives then the longitudinal forces at each individual corner of the vehicle can be estimated independently. Experimental and simulation results confirm the validity of the algorithm in slippery road conditions as well as normal conditions. The newly developed structure has a strong potential to be integrated with other state estimation, such as longitudinal/lateral velocities and lateral forces.
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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