Connected Automated Vehicle (CAV) applications are expected to transform the transportation landscape and address some of the pressing safety and efficiency issues. While advances in communication and computing technologies enable the concept of CAVs, the coupling of application, control and communication components of such systems and interference from human actors, pose significant challenges to designing systems that are safe and reliable beyond prototype environments. Realizing CAV applications, in particular in situations where humans may partly remain in the loop, requires addressing uncertainties that arise from human input. Large scale deployment of CAVs will also require addressing challenges in coordination of actions among CAVs and with human operated systems. To address these challenges, this project develops a novel model-based stochastic hybrid systems (SHS)-theoretic approach that relies on describing and communicating behaviors of actors in the system in the form of evolving SHS using Bayesian learning. The models are then utilized in a stochastic model predictive control (SMPC) framework for optimal coordination of actions. The proposed research will provide wide-ranging societal benefits through three major impact areas: first, by advancing research in stochastic communication-aware control design for hybrid systems; second, through the development of new models and advanced controllers to address the emerging challenges of coordinating mixed systems of automated and manned vehicles, hence opening new vistas in other areas involving general multi-agent systems; and third, through educational and outreach activities that are natural extensions of this multidisciplinary research. This project is also the first fruits of a recent National Science Foundation/Deutsche Forschungs Gesellschaft (NSF/DFG) collaboration on cyber-physical systems (CPS). Through this collaboration, NSF funds the US component (University of Central Florida and University of Georgia) while the German partners (University of Technology and University of Koblenz-Landau) are funded by DFG. The overarching goal of this collaborative research is to introduce SHS-based modeling and control concepts to allow the design of highly efficient CAV systems capable of large scale coordination (mass platooning). Such designs are currently challenging due to the uncertainties that stem from human input and communication of actors. The key objectives of the project are to: (1) develop methods for capturing the human, sensors and communication induced uncertainties of mixed automated and manned systems in a stochastic hybrid system form (perception maps) and communicating them in a control-aware fashion, (2) employ the models in an SMPC framework to produce multi-modal decisions and lower level longitudinal motion control in a single unified framework, and (3) validate the analytical outcomes through both extensive data-driven co-simulation using industry utilized models, and a fleet of realistic small CAVs and a full scale prototype CAV.