  Heterogeneous nature of the vehicular networks, which results from the
co-existence of human-driven, semi-automated, and fully autonomous vehicles, is
a challenging phenomenon toward the realization of the intelligent
transportation systems with an acceptable level of safety, comfort, and
efficiency. Safety applications highly suffer from communication resource
limitations, specifically in dense and congested vehicular networks. The idea
of model-based communication (MBC) has been recently proposed to address this
issue. In this work, we propose Gaussian Process-based Stochastic Hybrid System
with Cumulative Relevant History (CRH-GP-SHS) framework, which is a
hierarchical stochastic hybrid modeling structure, built upon a non-parametric
Bayesian inference method, i.e. Gaussian processes. This framework is proposed
in order to be employed within the MBC context to jointly model driver/vehicle
behavior as a stochastic object. Non-parametric Bayesian methods relieve the
limitations imposed by non-evolutionary model structures and enable the
proposed framework to properly capture different stochastic behaviors. The
performance of the proposed CRH-GP-SHS framework at the inter-mode level has
been evaluated over a set of realistic lane change maneuvers from NGSIM-US101
dataset. The results show a noticeable performance improvement for GP in
comparison to the baseline constant speed model, specifically in critical
situations such as highly congested networks. Moreover, an augmented model has
also been proposed which is a composition of GP and constant speed models and
capable of capturing the driver behavior under various network reliability
conditions.
