Braking and steering performance analysis of a road vehicle with active independent front steering
Bibliographic record
Abstract
Active front steering (AFS) systems help achieve target handling performance through simultaneous applications of steering corrections to both the wheels, but may exhibit limited performance during high-speed manoeuvres. This study explores the effectiveness of an active independent front steering (AIFS) system under application of braking to demonstrate that it could not only overcome the limitation of the AFS but also provide sufficient adhesion reserve for generating longitudinal forces. A simple AIFS controller is synthesised using the yaw rate feedback and the tyres' saturation limits. The simulation results are obtained under a wide range of braking-in-turn manoeuvres in different road conditions. Comparisons of the results with those obtained with the conventional AFS suggested greater effectiveness of the AIFS under high-speed manoeuvres. A parametric study is subsequently conducted to study the robustness of the AIFS performance. The results demonstrated enhanced braking-in-turn performance of the AIFS under conditions where the understeer handling characteristic exists.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".