IDDMBSE: Integrating Data-Driven and Model-Based Systems Engineering for Trusted Autonomous Cyber-Physical Systems

2026-06-04

AI Summary

This work addresses the current lack of native integration between model-based systems engineering (MBSE) and data-driven artificial intelligence, which hinders the full-lifecycle development of trustworthy autonomous cyber-physical systems. The paper proposes IDDMBSE, a novel methodology that embeds data-driven feedback loops into each phase of the MBSE V-model through SysML modeling, ROS autonomy stack mapping, and a hybrid architecture, enabling co-design, co-verification, and co-optimization of models and data. IDDMBSE establishes a pioneering systems engineering paradigm that deeply unifies MBSE with AI, supports natively composable domain-specific languages, and delivers an integrated assurance workflow spanning design, evaluation, and runtime verification. The approach is validated on trustworthy ground robots using the open-source toolchain—including PERFECT, TRADES-X, and VERITAS—demonstrating capabilities in sensor selection, risk-aware path planning, formal behavior tree verification, robust perception, multi-robot coordination, and an adversarial terrain testing environment released in Isaac Sim.

Abstract

Autonomous cyber-physical systems (CPS) sit at the intersection of Model-Based Systems Engineering (MBSE) and data-driven Machine Learning and Artificial Intelligence (ML/AI), yet no integrated Systems Engineering (SE) methodology natively spans both. We address this gap with IDDMBSE, an Integrated Data-Driven and Model-Based Systems Engineering methodology that extends the rigorous MBSE V-process with a data-driven loop at every step, anchored in SysML, the autonomy stack, and a hybrid model-based plus data-driven trade-off architecture. We instantiate IDDMBSE as an interoperable, open-source tool chain: PERFECT, which maps SysML system architectures to executable ROS autonomy stacks for scalable performance evaluation; TRADES-X, which decomposes design-space exploration into a model-based optimization stage followed by a data-driven evaluation stage; and VERITAS, which combines formal, data-driven, and runtime verification into a single assurance workflow. We demonstrate IDDMBSE on a Trusted Autonomous Ground Robot across its development lifecycle, spanning sensor-suite selection, risk-sensitive path planning, behavior-tree task verification, conformal-prediction-based robust perception, and assured multi-robot coordination, all exercised in a contested-terrain Isaac Sim test range that we release with the tool chain. We close by sketching how IDDMBSE is being re-formulated on SysML v2 / KerML foundations to enable language-native composability and tighter ML/AI integration.

Problem

• Model-Based Systems Engineering
• Data-Driven
• Autonomous Cyber-Physical Systems
• Systems Engineering
• Trusted Autonomy

Innovation

• IDDMBSE
• Model-Based Systems Engineering
• Data-Driven AI/ML
• SysML
• Trusted Autonomy