Design of autonomous driving controls for multi-trailer articulated heavy vehicles
Bibliographic record
Abstract
This article proposes a method for devising autonomous driving controls for multi-trailer articulated heavy vehicles (MTAHVs). This design is formulated as an optimization problem for improving ride quality and path-following performance. To implement the multi-objective design, a nonlinear model predictive control (NLMPC) technique is used to devise a tracking-controller for a MTAHV. For the NLMPC controller design, a nonlinear model is generated as the prediction model, and the respective TruckSim model is developed as the virtual plant. The weighting matrices of the NLMPC controller are chosen as the design variables, and a metaheuristic search algorithm is used to optimize these variables. By offline tuning these matrices automatically, the lateral-displacement error for the tractor decreases by 53%. Simulations demonstrate the reliability of the proposed design approach and the robustness of the NLMPC tracking-controller.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".