  Cooperative driving relies on communication among vehicles to create
situational awareness. One application of cooperative driving is Cooperative
Adaptive Cruise Control (CACC) that aims at enhancing highway transportation
safety and capacity. Model-based communication (MBC) is a new paradigm with a
flexible content structure for broadcasting joint vehicle-driver predictive
behavioral models. The vehicle's complex dynamics and diverse driving behaviors
add complexity to the modeling process. Gaussian process (GP) is a fully
data-driven and non-parametric Bayesian modeling approach which can be used as
a modeling component of MBC. The knowledge about the uncertainty is propagated
through predictions by generating local GPs for vehicles and broadcasting their
hyper-parameters as a model to the neighboring vehicles. In this research
study, GP is used to model each vehicle's speed trajectory, which allows
vehicles to access the future behavior of their preceding vehicle during
communication loss and/or low-rate communication. Besides, to overcome the
safety issues in a vehicle platoon, two operating modes for each vehicle are
considered; free following and emergency braking. This paper presents a
discrete hybrid stochastic model predictive control, which incorporates system
modes as well as uncertainties captured by GP models. The proposed control
design approach finds the optimal vehicle speed trajectory with the goal of
achieving a safe and efficient platoon of vehicles with small inter-vehicle gap
while reducing the reliance of the vehicles on a frequent communication.
Simulation studies demonstrate the efficacy of the proposed controller
considering the aforementioned communication paradigm with low-rate
intermittent communication.
