Design Optimization Of Autonomous Steering Control Schemes For Articulated Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a design synthesis approach for the development of autonomous steering control schemes for articulated vehicles. To design the autonomous steering controller, a 3 degrees of freedom (DOF) yaw-plane model is generated to represent a car-trailer combination, and a model predictive control (MPC) algorithm is used for lateral position and yaw motion control of the articulated vehicle. For enhancing the performance of the self-steering articulated vehicle, the design synthesis of the autonomous driving control schemes is formulated as a design optimization problem. Two optimization algorithms, namely Particle Swarm Optimization (PSO) and Differential Evolution (DE), are introduced and tested for the design optimization. In the design synthesis, the design variables may include passive vehicle design variables, e.g., geometric. To demonstrate the effectiveness of the proposed design synthesis approach, selected simulation results are presented and analyzed. The insightful findings attained from the study may be used as guidelines for developing autonomous driving control systems of articulated vehicles.
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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 it