Intention recognition-based braking strength control of an automotive magnetorheological fluid-based braking system
Bibliographic record
Abstract
Braking intention recognition plays a pivotal role in advancing braking assistant systems and enhancing driving safety, and is expected to be an essential component in intelligent automotive brake-by-wire systems. This paper proposes an intention recognition-based braking strength control algorithm that utilizes fuzzy logic to construct a robust control model. In addition, the braking intention parameters are selected according to the classification of braking status. The credibility of the proposed control algorithm is validated through comprehensive simulations, followed by its integration into the flywheel type 1/4 automotive magnetorheological fluid-based braking system test bench, equipped with a brake pedal simulator. Subsequently, braking control experiments are conducted within the established experimental system. Simulation results demonstrate that the braking strength control model realizes effective braking control and braking experiments indicate the accuracy of the braking intention recognition method. Importantly, the vehicle velocity variations under the proposed braking strength control model aligns with the expectation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".