Model Predictive Control for Reliable Path Following with Application to the Autonomous Vehicle and Considering Different Vehicle Models
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a reliable path following approach based on Model Predictive Control (MPC) and using different vehicle models are developed to: 1) minimize the lateral distance between the vehicle and a reference path, 2) minimize the heading error of the vehicle, and 3) limit the rate of steering inputs to their saturation values and produce smooth motions. The proposed MPC is implemented in MATLAB/Simulink using CasADi block in which the associated Optimal Control Problem (OCP) is discretized using the direct multiple-shooting method. Results show that designed controllers pulled the system back to the predefined reference path, and tracking performances were satisfactory. The effect of changing the prediction horizon and road friction coefficient at low/high speeds is discussed.
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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