A new technical paper, “Towards Structured Training and Validation of AI-based Systems with Digital Twin Scenarios,” was published by researchers at RWTH Aachen University and RIF e.V.
Abstract
“Artificial intelligence (AI) has emerged as a pivotal technology for autonomous systems across various domains, but quality assurance remains challenging due to limited training data and inadequate validation methods. This paper presents a framework combining scenario engineering and digital twins to address these challenges. It supports AI system development from synthetic data generation to validation within a unified simulation-based approach. Scenario engineering focuses on real-world scenarios, while digital twins provide virtual replicas of system components throughout their lifecycle. The framework ensures flexibility in system design and testing, offering structured and transparent processes for quality assurance workflows. Two application case studies are presented: training a LiDAR-based pose estimation model in aerospace and validating an autonomous driving function. These case studies demonstrate the applicability of the framework and its potential for different phases of system development.”
Find the technical paper here. March 2026.
O. Maqbool, U. Dahmen, M. Tesch, A. Kupetz and J. Roßmann, “Towards Structured Training and Validation of AI-based Systems with Digital Twin Scenarios,” in IEEE Open Journal of Intelligent Transportation Systems, doi: 10.1109/OJITS.2026.3670491.
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