  Cooperative driving, enabled by Vehicle-to-Everything (V2X) communication, is
expected to significantly contribute to the transportation system's safety and
efficiency. Cooperative Adaptive Cruise Control (CACC), a major cooperative
driving application, has been the subject of many studies in recent years. The
primary motivation behind using CACC is to reduce traffic congestion and
improve traffic flow, traffic throughput, and highway capacity. Since the
information flow between cooperative vehicles can significantly affect the
dynamics of a platoon, the design and performance of control components are
tightly dependent on the communication component performance. In addition, the
choice of Information Flow Topology (IFT) can affect certain platoons
properties such as stability and scalability. Although cooperative vehicles
perception can be expanded to multiple predecessors information by using V2X
communication, the communication technologies still suffer from scalability
issues. Therefore, cooperative vehicles are required to predict each other's
behavior to compensate for the effects of non-ideal communication. The notion
of Model-Based Communication (MBC) was proposed to enhance cooperative vehicles
perception under non-ideal communication by introducing a new flexible content
structure for broadcasting joint vehicles dynamic/drivers behavior models. By
utilizing a non-parametric (Bayesian) modeling scheme, i.e., Gaussian Process
Regression (GPR), and the MBC concept, this paper develops a discrete hybrid
stochastic model predictive control approach and examines the impact of
communication losses and different information flow topologies on the
performance and safety of the platoon. The results demonstrate an improvement
in response time and safety using more vehicles information, validating the
potential of cooperation to attenuate disturbances and improve traffic flow and
safety.
