Velocity Planning for Multi-Vehicle Systems via Distributed Optimization
Bibliographic record
Abstract
This work presents a distributed velocity planning strategy for multi-vehicle cooperation along pre-defined paths. Specifically, we consider a class of tasks where multiple vehicles must navigate given paths with conflict zones (e.g., merging and crossing) as fast as possible without any inner collisions. Given the paths, the biggest challenge for velocity planning is to create collision avoidance constraints without complete spatio-temporal information. To overcome the challenge, a scheme is proposed to project collision-related information from coordinate space to 1-D space with geometry-based safety guarantees. To enhance the ability to deal with medium-and large-scale problems, the alternating direction method of multipliers (ADMM) is introduced. Unlike classic ideas of applying distributed optimization, we formulate ADMM in a semi-centralized, semi-parallel fashion instead of a fully distributed fashion. In this way, a trade-off between overall performance and computational efficiency can be achieved. We evaluate our distributed planning strategy through simulations in multiple cases11Video of the results is available at https://youtu.be/GR6BwFTLErw. The results demonstrate that our method not only plans collision-free paths but also balances between overall performance and computational load.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".