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

A Reinforcement Learning Approach to Jointly Adapt Vehicular Communications and Planning for Optimized Driving