  In this paper, we present a data-driven Model Predictive Controller that
leverages a Gaussian Process to generate optimal motion policies for connected
autonomous vehicles in regions with uncertainty in the wireless channel. The
communication channel between the vehicles of a platoon can be easily
influenced by numerous factors, e.g. the surrounding environment, and the
relative states of the connected vehicles, etc. In addition, the trajectories
of the vehicles depend significantly on the motion policies of the preceding
vehicle shared via the wireless channel and any delay can impact the safety and
optimality of its performance. In the presented algorithm, Gaussian Process
learns the wireless channel model and is involved in the Model Predictive
Controller to generate a control sequence that not only minimizes the
conventional motion costs, but also minimizes the estimated delay of the
wireless channel in the future. This results in a farsighted controller that
maximizes the amount of transferred information beyond the controller's time
horizon, which in turn guarantees the safety and optimality of the generated
trajectories in the future. To decrease computational cost, the algorithm finds
the reachable set from the current state and focuses on that region to minimize
the size of the kernel matrix and related calculations. In addition, we present
an efficient recursive approach to decrease the time complexity of developing
the data-driven model and involving it in Model Predictive Control. We
demonstrate the capability of the presented algorithm in a simulated scenario.
