Towards Single-System Illusion in Software-Defined Vehicles --
  Automated, AI-Powered Workflow
Krzysztof Lebioda Viktor Vorobev Nenad Petrovic Fengjunjie Pan Vahid Zolfaghari Alois Knoll
We propose a novel model- and feature-based approach to development of
vehicle software systems, where the end architecture is not explicitly defined.
Instead, it emerges from an iterative process of search and optimization given
certain constraints, requirements and hardware architecture, while retaining
the property of single-system illusion, where applications run in a logically
uniform environment. One of the key points of the presented approach is the
inclusion of modern generative AI, specifically Large Language Models (LLMs),
in the loop. With the recent advances in the field, we expect that the LLMs
will be able to assist in processing of requirements, generation of formal
system models, as well as generation of software deployment specification and
test code. The resulting pipeline is automated to a large extent, with feedback
being generated at each step.