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

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.

Published in IEEE Transactions on Intelligent Vehicles, 29 July 2024

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• Comparison of Waymo Rider-Only Crash Data to Human Benchmarks at 7.1 Million Miles
• Benchmarks for Retrospective Automated Driving System Crash Rate Analysis Using Police-Reported Crash Data
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Cited by in corpus (1)

• Mcity Data Engine: Iterative Model Improvement Through Open-Vocabulary Data Selection