Rule-Based Cooperative Collision Avoidance Using Decentralized Model Predictive Control
Bibliographic record
Abstract
A rule-based decentralized model predictive control (DMPC) approach is employed to address the collision avoidance problem of multiple moving vehicles. Every vehicle uses model predictive control (MPC) to plan its trajectory towards its assigned target. The neighboring vehicles exchange their predicted trajectories at each sample time to predict the conflicts. Then, decentralized coordination and cooperation is performed to resolve the predicted conflicts. The Coordination part consists of online recalculation of the directed interaction graph topology to label conflicting vehicles as leader or follower. Between two conflicting vehicles, the vehicle with higher speed is labeled as leader and the other as follower. The Cooperation part consists of two simple rules, referred to as Heading-rule and Velocity-rule, which are often employed by human pedestrians to avoid potential collisions. The Heading-rule is first employed by both leader and follower to resolve the conflict. If it is not feasible to resolve the conflict by Heading-rule then the Velocity-rule is employed to decelerate the follower and accelerate the leader until the conflict is resolved. Numerous simulations of a team of unicycles are used to illustrate the proposed approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".