Provably Safe and Scalable Multivehicle Trajectory Planning
Bibliographic record
Abstract
The Hamilton–Jacobi (HJ) reachability is a promising tool for guaranteeing goal satisfaction and safety for multivehicle systems. However, a direct application of HJ reachability in most cases becomes intractable due to its exponentially scaling computational complexity with respect to the number of vehicles. In the work by Chenet al.(2018), the sequential trajectory planning (STP) method was proposed, which allows safe, multiple-vehicle trajectory planning to be done with a computation complexity that scales linearly with the number of vehicles. However, the STP computation is still not tractable for large-scale systems using the currently available tools. In this work, we introduce BEACLS, a C++-based reachability toolbox, that can leverage GPU parallelization to improve the computation speed of HJ reachability by nearly 100 times compared with the existing MATLAB implementations. We then combine BEACLS with STP for the safe, large-scale multiple-unmanned aerial vehicle (UAV) planning in a city environment and a multicity environment. We show that intuitive multilane structures naturally emerge, and the size of disturbances and the vehicle density are the primary factors determining the number and width of lanes. We also extend the STP method to safely account for an adversarial intruder during trajectory planning. In the proposed formulation, the number of vehicles that need to replan is a design parameter that can be chosen based on the computational resources available during run time. The proposed formulation along with BEACLS provides both an algorithm and an efficient computational tool for resilient, large-scale multiple-vehicle trajectory planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 0.001 |
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".