Design validation of active trailer steering systems for improving the low-speed manoeuvrability of multi-trailer articulated heavy vehicles using driver-hardware/software-in-the-loop real-time simulations
Bibliographic record
Abstract
This paper presents a design validation method for active trailer steering (ATS) for improving low-speed manoeuvrability of multi-trailer articulated heavy vehicles (MTAHVs) using driver-hardware/software-in-the-loop (DH/SIL) simulations. A numerical-optimisation-based design of a MTAHV with ATS was reported. The ATS is featured with two controllers, one for improving the manoeuvrability and the other for enhancing the stability. To validate the design for minimising the path-following off-tracking (PFOT), real-time simulations are conducted on a DH/SIL platform fabricated by combining a vehicle simulator with two physical ATS axles. The PFOT controller is reconstructed and integrated with the real-time MTAHV model for DH/SIL simulations. A benchmark is performed for three designs: 1) without the PFOT controller and the physical ATS axles; 2) only with the PFOT controller; 3) with both the PFOT controller and the ATS axles. The benchmark validates the reported design and demonstrates the effectiveness of the proposed design validation method.
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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 | 0.001 | 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 | 0.000 | 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".