Decentralized Model Predictive Control for Cooperative Multiple Vehicles Subject to Communication Loss
Why this work is in the frame
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Bibliographic record
Abstract
The decentralized model predictive control (DMPC) of multiple cooperative vehicles with the possibility of communication loss/delay is investigated. The neighboring vehicles exchange their predicted trajectories at every sample time to maintain the cooperation objectives. In the event of a communication loss (packet dropout), the most recent available information, which is potentially delayed, is used. Then the communication loss problem changes to a cooperative problem when random large communication delays are present. Such large communication delays can lead to poor cooperation performance and unsafe behaviors such as collisions. A new DMPC approach is developed to improve the cooperation performance and achieve safety in the presence of the large communication delays. The proposed DMPC architecture estimates the tail of neighbor's trajectory which is not available due to the large communication delays for improving the performance. The concept of the tube MPC is also employed to provide the safety of the fleet against collisions, in the presence of large intervehicle communication delays. In this approach, a tube shaped trajectory set is assumed around the trajectory of the neighboring vehicles whose trajectory is delayed/lost. The radius of tube is a function of the communication delay and vehicle's maneuverability (in the absence of model uncertainty). The simulation of formation problem of multiple vehicles is employed to 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 | 0.000 | 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