Real-Time MPC for WATonoBus Path Tracking
Bibliographic record
Abstract
In this paper, a real-time Model Predictive Controller (MPC) tailored for precise path tracking in congested urban traffic environments is developed. Leveraging a front and rear steering dynamic bicycle model, along with a linear tire model, the controller aims to reduce system dynamic complexity to ensure computational feasibility for real-time operation. The proposed MPC formulation features a quadratic cost function, solved using a standard quadratic programming solver, to optimize steering inputs over a prediction horizon. Experimental validation on the WATonoBus platform demonstrates the controller's effectiveness in guiding the vehicle along desired trajectories with precision. Precise tracking is especially important in urban driving scenarios such as merging, and pullover, where safety and avoidance of pedestrians and curbs is paramount. Additionally, a framework for augmenting the vehicle prediction model using data-driven techniques is explored. This framework provides the ability to account for unmodelled system dynamics while maintaining the real-time capability of the controller. Overall, this work showcases the practical applicability of an MPC controller for real-world autonomous driving scenarios.
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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 | 0.001 | 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 | 0.001 |
| 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".