  Our premise is that autonomous vehicles must optimize communications and
motion planning jointly. Specifically, a vehicle must adapt its motion plan
staying cognizant of communications rate related constraints and adapt the use
of communications while being cognizant of motion planning related restrictions
that may be imposed by the on-road environment. To this end, we formulate a
reinforcement learning problem wherein an autonomous vehicle jointly chooses
(a) a motion planning action that executes on-road and (b) a communications
action of querying sensed information from the infrastructure. The goal is to
optimize the driving utility of the autonomous vehicle. We apply the Q-learning
algorithm to make the vehicle learn the optimal policy, which makes the optimal
choice of planning and communications actions at any given time. We demonstrate
the ability of the optimal policy to smartly adapt communications and planning
actions, while achieving large driving utilities, using simulations.
