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 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 | 0.001 |
| 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 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".