  Scalable communication is of utmost importance for reliable dissemination of
time-sensitive information in cooperative vehicular ad-hoc networks (VANETs),
which is, in turn, an essential prerequisite for the proper operation of the
critical cooperative safety applications. The model-based communication (MBC)
is a recently-explored scalability solution proposed in the literature, which
has shown a promising potential to reduce the channel congestion to a great
extent. In this work, based on the MBC notion, a technology-agnostic hybrid
model selection policy for Vehicle-to-Everything (V2X) communication is
proposed which benefits from the characteristics of the non-parametric Bayesian
inference techniques, specifically Gaussian Processes. The results show the
effectiveness of the proposed communication architecture on both reducing the
required message exchange rate and increasing the remote agent tracking
precision.
