An automated design synthesis method for multi-trailer articulated heavy vehicles
Bibliographic record
Abstract
This paper presents an automated design synthesis method for multi-trailer articulated heavy vehicles (MTAHVs) to coordinate the trade-off between the manoeuvrability and stability. Conventionally, the design synthesis of MTAHVs is based on trial and error approaches. This is difficult, time-consuming and tedious. To tackle this problem, a design method is proposed. The method has the following features: 1) vehicle modelling, performance evaluation, and design selection are implemented by computers; 2) a MTAHV model is introduced in the design optimisation; 3) in the closed-loop simulation, testing manoeuvres are emulated such that a driver model ‘drives’ the virtual MTAHV to follow a predefined trajectory. To test the method, the combination of a tractor and two semitrailers are optimised. It is indicated that the conflicting criteria of manoeuvrability and stability can be simultaneously improved. The approach may identify desired design variables and predict performance envelopes in early design stages of MTAHVs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".