An Adaptive and Fast Control Strategy for Antilock Braking System
Bibliographic record
Abstract
After more than 30 years since the Antilock Braking System (ABS) was first introduced, it has become the most important active safety system used on passenger cars. However, it is hard to find a precise description of ABS, its stability and performance in the literature. Most of ABS algorithms currently used are not adaptive to changes of road friction conditions. The aim of our work is to provide a new ABS algorithm that is adaptive to changes of road conditions. To this end, an online parameter estimator is designed to estimate the road characteristics and maximum possible deceleration. Then, a driver demand regulator is proposed to limit the demanded deceleration to the maximum values. In this new strategy, road characteristics are estimated prior to the braking, not during the braking which makes it fast and adaptive. The proposed ABS algorithm is simulated on an artificial driving track and simulation results have been compared to a simple non-adaptive 6-phase Bosch ABS algorithm as our benchmark that is based on deceleration thresholds. Results show a better braking performance and more than 30% of reduction in braking distance.
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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 | 0.000 |
| 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".