Integrated Combined-Slip-Based Vehicle and Wheel Dynamic Control for Electric Vehicles
Bibliographic record
Abstract
A vehicle stabilization control system that integrates vehicle lateral and wheel dynamics and considers a tire combined-slip model for automated driving systems in electric vehicles, is presented. This paper introduces a new prediction model that not only considers lateral force drop by the longitudinal slip, but also takes into account the variation of longitudinal forces due to slip angles during cornering. As confirmed by road experiments as well as high-fidelity simulations, this novel model derivation improves the lateral stability of the autonomous driving significantly through more feasible control actuation satisfying safety and stability constraints. The control system monitors cornering and brake force capacities without having road surface friction information and adjusts slips to minimize tracking error, satisfying stability requirements through a constrained optimization program. The stability of the proposed receding horizon is proved, and the performance of the controller is evaluated in road experiments, in real-time, in various maneuvers on different road surfaces.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".