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arXiv · 2304.01040

Safety Under Uncertainty: Tight Bounds with Risk-Aware Control Barrier Functions

Mitchell Black·Georgios Fainekos·Bardh Hoxha·Danil Prokhorov·Dimitra Panagou
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Abstract

We propose a novel class of risk-aware control barrier functions (RA-CBFs) for the control of stochastic safety-critical systems. Leveraging a result from the stochastic level-crossing literature, we deviate from the martingale theory that is currently used in stochastic CBF techniques and prove that a RA-CBF based control synthesis confers a tighter upper bound on the probability of the system becoming unsafe within a finite time interval than existing approaches. We highlight the advantages of our proposed approach over the state-of-the-art via a comparative study on an mobile-robot example, and further demonstrate its viability on an autonomous vehicle highway merging problem in dense traffic.

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Mitchell Black, Georgios Fainekos, Bardh Hoxha, Danil Prokhorov, Dimitra Panagou. 2023-04-03. Safety Under Uncertainty: Tight Bounds with Risk-Aware Control Barrier Functions. https://doi.org/10.1109/icra48891.2023.10161379

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