  The performance of cooperative vehicular applications is tightly dependent on
the reliability of the underneath Vehicle-to-Everything (V2X) communication
technology. V2X standards, such as Dedicated Short-Range Communications (DSRC)
and Cellular-V2X (C-V2X), which are passing their research phase before being
mandated in the US, are supposed to serve as reliable circulatory systems for
the time-critical information in vehicular networks; however, they are still
heavily suffering from scalability issues in real traffic scenarios. The
technology-agnostic notion of Model-Based Communications (MBC) has been
proposed in our previous works as a promising paradigm to address the
scalability issue and its performance, while acquiring different modeling
strategies, has been vastly studied. In this work, the modeling capabilities of
a powerful non-parametric Bayesian inference scheme, i.e., Gaussian Processes
(GPs), is investigated within the MBC context with more details. Our
observations reveal an important potential strength of GP-based MBC scheme,
i.e., its capability of accurately modeling different driving behavioral
patterns by utilizing only a limited size GP kernel bank. This interesting
aspect of integrating GP inference with MBC framework, which has been verified
in this work using realistic driving data sets, introduces this architecture as
a strong and appealing candidate to address the scalability challenge. The
results confirm that our proposed approach over-performs the state of the art
research in terms of the required communication rate and GP kernel bank size.
