Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies

TL;DR

This paper introduces a reinforcement learning framework that optimizes autonomous vehicle driving and network selection policies simultaneously, improving safety, traffic flow, and communication data rates in multi-band vehicular networks.

Contribution

It develops a deep Q-learning based approach to jointly optimize vehicle motion and network choices, addressing uncertainties in V2I communication and traffic conditions.

Findings

Enhanced vehicle safety and connectivity through RL policies.

Improved data rates and reduced handoffs in multi-band V2I networks.

Insights into the interplay between vehicle dynamics and communication performance.

Abstract

Vehicle-to-Infrastructure (V2I) communication is becoming critical for the enhanced reliability of autonomous vehicles (AVs). However, the uncertainties in the road-traffic and AVs' wireless connections can severely impair timely decision-making. It is thus critical to simultaneously optimize the AVs' network selection and driving policies in order to minimize road collisions while maximizing the communication data rates. In this paper, we develop a reinforcement learning (RL) framework to characterize efficient network selection and autonomous driving policies in a multi-band vehicular network (VNet) operating on conventional sub-6GHz spectrum and Terahertz (THz) frequencies. The proposed framework is designed to (i) maximize the traffic flow and minimize collisions by controlling the vehicle's motion dynamics (i.e., speed and acceleration) from autonomous driving perspective, and (ii) maximize the data rates and minimize handoffs by jointly controlling the vehicle's motion dynamics and network selection from telecommunication perspective. We cast this problem as a Markov Decision Process (MDP) and develop a deep Q-learning based solution to optimize the actions such as acceleration, deceleration, lane-changes, and AV-base station assignments for a given AV's state. The AV's state is defined based on the velocities and communication channel states of AVs. Numerical results demonstrate interesting insights related to the inter-dependency of vehicle's motion dynamics, handoffs, and the communication data rate. The proposed policies enable AVs to adopt safe driving behaviors with improved connectivity.