  We address the problem of controlling Connected and Automated Vehicles (CAVs)
in conflict areas of a traffic network subject to hard safety constraints. It
has been shown that such problems can be solved through a combination of
tractable optimal control problems and Control Barrier Functions (CBFs) that
guarantee the satisfaction of all constraints. These solutions can be reduced
to a sequence of Quadratic Programs (QPs) which are efficiently solved on line
over discrete time steps. However, guaranteeing the feasibility of the
CBF-based QP method within each discretized time interval requires the careful
selection of time steps which need to be sufficiently small. This creates
computational requirements and communication rates between agents which may
hinder the controller's application to real CAVs. In this paper, we overcome
this limitation by adopting an event-triggered approach for CAVs in a conflict
area such that the next QP is triggered by properly defined events with a
safety guarantee. We present a laboratory-scale test bed we have developed to
emulate merging roadways using mobile robots as CAVs which can be used to
demonstrate how the event-triggered scheme is computationally efficient and can
handle measurement uncertainties and noise compared to time-driven control
while guaranteeing safety.
