  This paper proposes a safety-critical controller for dynamic and uncertain
environments, leveraging a robust environment control barrier function (ECBF)
to enhance the robustness against the measurement and prediction uncertainties
associated with moving obstacles. The approach reduces conservatism, compared
with a worst-case uncertainty approach, by incorporating a state observer for
obstacles into the ECBF design. The controller, which guarantees safety, is
achieved through solving a quadratic programming problem. The proposed method's
effectiveness is demonstrated via a dynamic obstacle-avoidance problem for an
autonomous vehicle, including comparisons with established baseline approaches.
