  In this paper, we study the role that machine learning can play in
cooperative driving. Given the increasing rate of connectivity in modern
vehicles, and road infrastructure, cooperative driving is a promising first
step in automated driving. The example scenario we explored in this paper, is
coordinated lane merge, with data collection, test and evaluation all conducted
in an automotive test track. The assumption is that vehicles are a mix of those
equipped with communication units on board, i.e. connected vehicles, and those
that are not connected. However, roadside cameras are connected and can capture
all vehicles including those without connectivity. We develop a Traffic
Orchestrator that suggests trajectories based on these two sources of
information, i.e. connected vehicles, and connected roadside cameras.
Recommended trajectories are built, which are then communicated back to the
connected vehicles. We explore the use of different machine learning techniques
in accurately and timely prediction of trajectories.
