Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies. (arXiv:2208.02249v1 [cs.LG])

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.



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