most citedDefining Operational Conditions for Safety-Critical AI-Based Systems from Data

1 citations · 1 across the 5 of their papers we have counts for

collaborators

7 papers

cs.LG2026

Reinforcement Learning Inspired Black-box Adversarial Attacks for Computer Vision

Florian Krone, Elena Hoemann, Sven Hallerbach

Neural networks, both convolution or transformer based, are essential for modern computer vision systems. However, they are vulnerable to small perturbations, almost imperceptible…

cs.CV2026

Mitigating Shortcut Learning: Texture-Penalized Prototype Networks

Akshay Anilkumar Girija, Elena Hoemann, Frank Köster +1

Standard Convolutional Neural Networks (CNNs) exhibit severe performance degradation due to a strong inductive texture bias that prioritizes local, high-frequency patterns over glo…

cs.AI2026

Coverage-Driven Verification for Safety-by-Design in AI-Based Collision Avoidance Systems

Thomas Stefani, Johann Maximilian Christensen, Elena Hoemann +2

Artificial Intelligence (AI) offers significant potential for future aviation systems; however, its integration into safety-critical applications requires compliance with the aviat…

cs.AI2026

On the Applicability of Safety Nets: A Safety-By-Design Solution for Certifying Neural Networks

Johann Maximilian Christensen, Thomas Stefani, Elena Hoemann +2

The integration of Artificial Intelligence (AI) in safety-critical aviation systems presents significant challenges for certification and deployment. Aviation, often regarded as th…

cs.CV2026

Image Quality Dependent Degradation for AI Systems

Yannick Kees, Elena Hoemann, Frank Köster +1

Perception is one of the primary applications where neural networks outperform conventional algorithms. One example is AI systems for automated driving, which can detect pedestrian…

cs.AI2026

From High-Dimensional Spaces to Verifiable ODD Coverage for Safety-Critical AI-based Systems

Thomas Stefani, Johann Maximilian Christensen, Elena Hoemann +2

While Artificial Intelligence (AI) offers transformative potential for operational performance, its deployment in safety-critical domains such as aviation requires strict adherence…

cs.AI2026★ 1 cited

Defining Operational Conditions for Safety-Critical AI-Based Systems from Data

Johann Maximilian Christensen, Elena Hoemann, Frank Köster +1

Artificial Intelligence (AI) has been on the rise in many domains, including numerous safety-critical applications. However, for complex systems in the real world, defining the und…