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

TL;DR

This paper proposes a reinforcement learning framework for autonomous vehicles to jointly optimize motion planning and communication strategies, enhancing driving utility through adaptive decision-making in simulated environments.

Contribution

It introduces a novel joint optimization approach using Q-learning for autonomous vehicle communication and planning, addressing their interdependence in a unified framework.

Findings

The optimal policy adapts communication and planning actions effectively.

Simulations show significant improvements in driving utility.

The approach demonstrates smart adaptation in complex scenarios.

Abstract

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.