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

2025-02-18

IEEE Transactions on Intelligent Vehicles

AI Summary

Traditional V-models are inadequate for managing the full lifecycle of highly autonomous cognitive cyber-physical systems (e.g., autonomous driving), particularly in data-driven development, iterative evolution, and safety assurance. Method: This paper proposes an extended V-shaped framework that formally integrates an iterative, data-driven paradigm into the classical V-model for the first time—unifying usage protocols for both synthetic and real-world data—and synergistically incorporates development, verification & validation (V&V), and continuous integration/continuous verification (CI/CV) practices. Contribution/Results: The resulting reusable, general-purpose framework significantly enhances traceability, verifiability, and regulatory compliance in AI system development. It provides both a methodological foundation and practical implementation pathways for end-to-end safety management across the lifecycle of highly autonomous systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Computer Vision: Vision for Robotics & Autonomous Driving
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Security and Privacy: Data transparency and provenance
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Extend V-model for AI system development
• Integrate iterative data-based processes
• Ensure safety of complex AI systems

Innovation

• Extended V-model framework
• Iterative data-based development
• Formalized AI integration process