A data-driven CBF converts alpha-confidence sets on unknown obstacle dynamics into probabilistic safety guarantees for vehicles with arbitrary relative-degree dynamics.
Characterizing smooth safety filters via the implicit function theorem
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A new collaborative safety framework for multi-agent systems uses control barrier functions and Hamilton's rule to let agents trade off their own safety for higher-priority neighbors.
citing papers explorer
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CBF-based Probabilistic Safe Navigation under Unknown Nonlinear Obstacle Dynamics
A data-driven CBF converts alpha-confidence sets on unknown obstacle dynamics into probabilistic safety guarantees for vehicles with arbitrary relative-degree dynamics.
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Collaborative Altruistic Safety in Coupled Multi-Agent Systems
A new collaborative safety framework for multi-agent systems uses control barrier functions and Hamilton's rule to let agents trade off their own safety for higher-priority neighbors.