An overview of control schemes for improving the lateral stability of car-trailer combinations
Bibliographic record
Abstract
This paper reviews the state-of-the-art control schemes for enhancing the lateral stability of car-trailer (CT) combinations. Various studies have been conducted on lateral stability control of single-unit vehicles, e.g., cars. However, much less attention has been paid to lateral stability control of multi-unit vehicles, e.g., CT, which usually exhibit poor manoeuvrability in curved-path negotiations and low lateral stability under high-speed evasive manoeuvres. The low lateral stability may lead to unstable motion modes, e.g., trailer-sway and jackknifing, causing severe accidents. To improve the lateral stability, various control schemes were designed considering relevant performance measures and evaluated using either numerical simulations or testing methods. Thus, the topics surveyed in this paper include: directional performance measures, evaluation methods, important parameters affecting the directional performance, and active control approaches for CT combinations. Important control schemes are emphasised and their features discussed and analysed. As a result of the overview, future research efforts are identified.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".