  Addressing safe and efficient interaction between connected and automated
vehicles (CAVs) and human-driven vehicles in a mixed-traffic environment has
attracted considerable attention. In this paper, we develop a framework for
stochastic time-optimal trajectory planning for coordinating multiple CAVs in
mixed-traffic merging scenarios. We present a data-driven model, combining
Newell's car-following model with Bayesian linear regression, for efficiently
learning the driving behavior of human drivers online. Using the prediction
model and uncertainty quantification, a stochastic time-optimal control problem
is formulated to find robust trajectories for CAVs. We also integrate a
replanning mechanism that determines when deriving new trajectories for CAVs is
needed based on the accuracy of the Bayesian linear regression predictions.
Finally, we demonstrate the performance of our proposed framework using a
realistic simulation environment.
