From the 1 of 6 linked papers with an AI index.
1 citations · 1 across the 3 of their papers we have counts for
6 papers
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…
Image Quality Dependent Degradation for AI Systems
Yannick Kees, Elena Hoemann, Frank Köster +1
The paper proposes a method to estimate image quality using normalizing flows and adjust object detection confidence thresholds, enabling safer AI-driven perception in low-quality…
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…
Learning to Reason: Targeted Knowledge Discovery and Fuzzy Logic Update for Robust Image Recognition
Gurucharan Srinivas, Joshua Niemeijer, Frank Köster
Integrating domain knowledge into deep neural networks is a promising way to improve generalization. Existing methods either encode prior knowledge in the loss function or apply po…
Revisiting Neural Activation Coverage for Uncertainty Estimation
Benedikt Franke, Nils Förster, Frank Köster +3
Neural activation coverage (NAC) is a recently-proposed technique for out-of-distribution detection and generalization. We build upon this promising foundation and extend the metho…
From High-Dimensional Spaces to Verifiable ODD Coverage for Safety-Critical AI-based Systems
Thomas Stefani, Johann Maximilian Christensen, Elena Hoemann +3
While Artificial Intelligence (AI) offers transformative potential for operational performance, its deployment in safety-critical domains such as aviation requires strict adherence…