Path-following control of autonomous ground vehicles based on input convex neural networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper studies the path-following problems in autonomous ground vehicles (AGVs) through predictive control and neural network modeling. Considering the model of AGVs is usually difficult to construct by first principles accurately, a data-driven approach based on deep neural networks is proposed to deal with the system identification tasks. Although deep neural networks have good representation capability for complex system, they are still hard to use for control area due to their nonconvexities and nonlinearities. Therefore, to make a trade-off between control tractability and model accuracy, the input convex neural networks (ICNNs) are developed to describe the dynamics of AGVs. As the designed neural networks are convex with regard to the inputs, the predictive control problem is converted to a convex optimization problem and thus it’s easier to get feasible solutions. Besides, for adapting to different road conditions and some other disturbances, a periodically online learning algorithm is designed to update the neural network. Finally, two driving simulations under CarSim-Simulink platform are conducted to prove the superiority of our proposed techniques.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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