The ability of connected and autonomous vehicles (CAVs) to share information such as road friction and geometry has the potential to improve the safety, capacity, and efficiency of roadway systems, and the study of these systems often necessitates synergistic investigation of the vehicle, traffic behavior, and road conditions. This paper presents a micro-simulation framework for studying CAVs behavior and control utilizing a traffic simulator, chassis simulation, and a shared roadway friction database. The simulation utilizes three levels of data representations: 1) a traffic representation that explains how vehicles interact with each other and follow location-specific rules of the road, 2) a vehicle dynamic representation of the Newtonian response of the vehicle to driver inputs interacting with the vehicle which in turn interacts with the pavement, and finally 3) a road surface representation that represents how friction of roadway changes with space and time. The interactions between these representations are mediated by a spatiotemporal database. The framework is demonstrated through a CAVs application example showing how the mapping of road friction enables advanced vehicle control by allowing the database-mediated preview of road friction. This framework extends readily to real-time implementation on actual CAVs systems, providing great potential for improving CAVs control performance and stability via database-mediated feedback systems, not only in simulation, but also in practice.