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Cooperative driving relies on communication among vehicles to createsituational awareness. One application of cooperative driving is CooperativeAdaptive Cruise Control (CACC) that aims at enhancing highway transportationsafety and capacity. Model-based communication (MBC) is a new paradigm with aflexible content structure for broadcasting joint vehicle-driver predictivebehavioral models. The vehicle's complex dynamics and diverse driving behaviorsadd complexity to the modeling process. Gaussian process (GP) is a fullydata-driven and non-parametric Bayesian modeling approach which can be used asa modeling component of MBC. The knowledge about the uncertainty is propagatedthrough predictions by generating local GPs for vehicles and broadcasting theirhyper-parameters as a model to the neighboring vehicles. In this researchstudy, GP is used to model each vehicle's speed trajectory, which allowsvehicles to access the future behavior of their preceding vehicle duringcommunication loss and/or low-rate communication. Besides, to overcome thesafety issues in a vehicle platoon, two operating modes for each vehicle areconsidered; free following and emergency braking. This paper presents adiscrete hybrid stochastic model predictive control, which incorporates systemmodes as well as uncertainties captured by GP models. The proposed controldesign approach finds the optimal vehicle speed trajectory with the goal ofachieving a safe and efficient platoon of vehicles with small inter-vehicle gapwhile reducing the reliance of the vehicles on a frequent communication.Simulation studies demonstrate the efficacy of the proposed controllerconsidering the aforementioned communication paradigm with low-rateintermittent communication.

Gaussian Process based Stochastic Model Predictive Control for Cooperative Adaptive Cruise Control