Jackknifing Prevention of Tractor-Semitrailer Combination Using Active Braking Control
Bibliographic record
Abstract
Vehicle jackknifing is generally associated with the loss of yaw stability, and is one of the most common cause of serious traffic accidents involving tractor-semitrailer combinations. In this paper, an active braking control strategy is proposed for jackknifing prevention of a tractor-semitrailer combination on a low friction road. The proposed control strategy is realized via upper-level and lower-level control structures considering braking of both the units. In the upper-level control, the required corrective yaw moments for tractor and semitrailer are generated using a PID controller aiming to reduce errors between the actual yaw rates of tractor-semitrailer and the target yaw rates deduced from a reference model. The corrective yaw moments are achieved through brake torque distribution among the tractor and semitrailer axle wheels in the lower-level control. The effectiveness of the proposed jackknifing prevention control is evaluated in a co-simulation environment involving Matlab/Simulink and TruckSim under two different maneuvers on a slippery road surface. Simulation results show that the proposed control approach is effective in jackknifing prevention of the tractor-semitrailer combinations under high speed maneuvers on low friction surfaces.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".