In this paper, we present a two-level optimal control framework to address
motion coordination of connected automated vehicles (CAVs) in the presence of
human-driven vehicles (HDVs) in merging scenarios. Our framework combines an
unconstrained trajectory solution of a low-level energy-optimal control problem
with an upper-level optimization problem that yields the minimum travel time
for CAVs. We predict the future trajectories of the HDVs using Newell's
car-following model. To handle potential deviations of HDVs' actual behavior
from the one predicted, we provide a risk-triggered re-planning mechanism for
the CAVs based on time-to-conflict. The effectiveness of the proposed control
framework is demonstrated via simulations with heterogeneous human driving
behaviors and via experiments in a scaled environment.Comment: first manuscript, 7 page