A data-driven CBF converts alpha-confidence sets on unknown obstacle dynamics into probabilistic safety guarantees for vehicles with arbitrary relative-degree dynamics.
Observer-based environment robust control barrier functions for safety-critical control with dynamic obstacles
2 Pith papers cite this work. Polarity classification is still indexing.
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eess.SY 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
The authors introduce an output-feedback CBF framework using expectation-based barrier conditions and Jensen inequality bounds to conservatively enforce safety in stochastic systems with partial observations.
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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Output-Feedback Safe Control of Discrete-Time Stochastic Systems with Chance Constraints
The authors introduce an output-feedback CBF framework using expectation-based barrier conditions and Jensen inequality bounds to conservatively enforce safety in stochastic systems with partial observations.