Automated Design Synthesis of Articulated Heavy Vehicles With Active Trailer Steering Systems
Bibliographic record
Abstract
This paper presents an automated design synthesis approach for articulated heavy vehicles (AHVs) with active trailer steering (ATS) systems. AHVs have poor maneuverability when traveling at low speeds. Moreover, AHVs exhibit unstable motion modes at high speeds. To address the problem of maneuverability, ‘passive’ trailer steering systems have been developed. These systems improve low-speed performance, but feature with low lateral stability at high speeds. Some ATS systems have been proposed to improve highspeed lateral stability. However, these systems typically degrade maneuverability when applied at low speeds. To tackle this conflicting design problem, a systematic method is proposed for the design of AHVs with ATS systems. This new design method has the following features: the optimal active design variables of the ATS systems and the optimal passive design variables of the vehicle are identified in a single design loop; in the design process, to evaluate the vehicle performance measures, a driver model is introduced and it ‘drives’ the vehicle model based on the well-defined testing specifications. Through the design optimization of an ATS system for an AHV with a tractor and a full trailer, this single design loop (SDL) method is compared against a published two design loop (TDL) method. The benchmark investigation shows that the former can determine better trade-off design solutions than those derived by the latter. This SDL method provides an effective approach to automatically implement the design synthesis of AHVs with ATS systems.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".