Distributed Learning for Vehicular Dynamic Spectrum Access in Autonomous Driving

TL;DR

This paper proposes a distributed Q-learning approach for autonomous vehicles to dynamically select communication channels, enhancing safety and reliability in vehicle platooning amidst increasing traffic and spectrum congestion.

Contribution

It introduces a lightweight, decentralized Q-learning method for intra-platoon spectrum management, integrating roadside database support with AI-driven decision-making.

Findings

Effective dynamic channel selection in autonomous vehicle platooning.

Improved communication reliability under spectrum congestion.

Feasible implementation of lightweight Q-learning in vehicles.

Abstract

Reliable wireless communication between the autonomously driving cars is one of the fundamental needs for guaranteeing passenger safety and comfort. However, when the number of communicating cars increases, the transmission quality may be significantly degraded due to too high occupancy radio of the used frequency band. In this paper, we concentrate on the autonomous vehicle-platooning use-case, where intra-platoon communication is done in the dynamically selected frequency band, other than nominally devoted for such purposes. The carrier selection is done in a flexible manner with the support of the context database located at the roadside unit (edge of wireless communication infrastructure). However, as the database delivers only context information to the platoons' leaders, the final decision is made separately by the individual platoons, following the suggestions made by the artificial intelligence algorithms. In this work, we concentrate on a lightweight Q-learning solution, that could be successfully implemented in each car for dynamic channel selection.