Architecture for testing learning-based autonomous vehicle control design
Bibliographic record
Abstract
This paper presents the architecture for the testing of autonomous vehicle controllers designed using machine learning. A localization system feeds measurement data into a ground station. The ground station processes this data and provides the position of the vehicle to the controller, which has been tuned offline via machine learning. Control inputs are then generated and provided to the vehicle in order to accomplish the desired mission. The performance of the learned controller is compared to a nominal case. During the offline tuning process, a vehicle model is used to simulate the actual vehicle. Once tuning is complete, the parameters are used to control a real vehicle in order to accomplish the desired mission. The proposed architecture was tested by tuning a model predictive controller to guide a differential drive robot to a series of waypoints using a Reinforced Learning technique known as Learning Automata. The controller was then tested online with a similar but different problem on a real differential drive robot. The results showed that after tuning, the vehicle performed significantly better. This demonstrated that offline learning techniques can be used to optimally select controller parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".