Distributed Learning for Vehicular Dynamic Spectrum Access in Autonomous Driving
This work addresses communication reliability for autonomous driving safety, but it is incremental as it applies an existing Q-learning method to a specific vehicular scenario.
The paper tackles the problem of reliable wireless communication in autonomous vehicle platoons by proposing a lightweight Q-learning solution for dynamic channel selection, which improves transmission quality as the number of communicating cars increases.
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.