  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.
