Expanding the Classical V-Model for the Development of Complex Systems Incorporating AI

TL;DR

This paper extends the classical V-model to better support the development of complex autonomous systems with AI, emphasizing iterative, data-driven processes for safety and formalization.

Contribution

It introduces a formalized, iterative extension of the V-model that integrates data-based development, verification, and validation for AI-enabled complex systems.

Findings

Provides a unified framework for AI system development

Formalizes data-driven development and validation processes

Enhances safety assurance for autonomous systems

Abstract

Research in the field of automated vehicles, or more generally cognitive cyber-physical systems that operate in the real world, is leading to increasingly complex systems. Among other things, artificial intelligence enables an ever-increasing degree of autonomy. In this context, the V-model, which has served for decades as a process reference model of the system development lifecycle is reaching its limits. To the contrary, innovative processes and frameworks have been developed that take into account the characteristics of emerging autonomous systems. To bridge the gap and merge the different methodologies, we present an extension of the V-model for iterative data-based development processes that harmonizes and formalizes the existing methods towards a generic framework. The iterative approach allows for seamless integration of continuous system refinement. While the data-based approach constitutes the consideration of data-based development processes and formalizes the use of synthetic and real world data. In this way, formalizing the process of development, verification, validation, and continuous integration contributes to ensuring the safety of emerging complex systems that incorporate AI.