Safe Path Planning in the Presence of Large Communication Delays Using Tube Model Predictive Control
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a conflict resolution problem for multiple vehicles subject to large communication delays is investigated in a decentralized model predictive control (DMPC) framework. Using model predictive control, each vehicle plans its own future path towards its assigned target and predicts the possible collisions with neighboring vehicles. The planned trajectories are then redefined to resolve possible collisions. Conflict prediction and resolution is accomplished by providing cooperation and coordination among the vehicles. The cooperation part involves exchanging the planned trajectories at each sample time among the vehicles. The coordination part consists of imposing a maneuverability constraint in each vehicle’s optimization problem. The possible collisions are determined by calculating the neighboring vehicle’s reachable set consisting of a tube shaped trajectory around the neighbor’s trajectory. To resolve potential conflicts each vehicle predicting a possible collision restricts its maneuverability so that its calculated tube does not intersect with those of its neighbors. Computer simulations illustrate the effectiveness of the proposed approach.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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