The development of cyber-physical systems poses a multitude of challenges requiring experts from different fields. Such systems cannot be developed successfully without the support of appropriate processes, languages, and tools. Model-driven software engineering is an important approach which helps development teams to cope with the increasing complexity of today's cyber-physical systems. The aim of this thesis is to develop a model-driven engineering methodology with a particular focus on interconnected intelligent cyber-physical systems such as cooperative vehicles. The basis of the proposed methodology is a component-and-connector architecture description language. It features a strong, math-oriented type system abstracting from the technical realization and incorporating physical units. To facilitate the development of self-adaptive systems, the language enables its users to model component and connector arrays and supports architectural runtime reconfiguration. Architectural elements can be altered, added, and removed dynamically upon the occurrence of trigger events. Furthermore, the proposed methodology provides means for behavior specification and its seamless integration into the software architecture. A matrix-oriented scripting language enables the developer to specify algorithms using a syntax close to the mathematical domain. A dedicated deep learning modeling language is provided for the development and training of neural networks as directed acyclic graphs of neuron layers. The framework supports different learning methods including supervised, reinforcement, and generative adversarial learning, covering a broad range of applications from image and natural language processing to decision making and test data generation.