arXiv AI
Defining Operational Conditions for Safety-Critical AI-Based Systems from Data
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arXiv AI
Sep 3
From High-Dimensional Spaces to Verifiable ODD Coverage for Safety-Critical AI-based Systems
The paper proposes a structured method for verifying Operational Design Domain (ODD) coverage in safety‑critical AI systems, particularly for aviation. It combines parameter discretization, constraint‑based filtering, and criticality‑based dimension reduction to create a multi‑step verification process. Using simulation data from AI‑based mid‑air collision avoidance research, the authors demonstrate how this approach can meet EASA’s requirement for complete ODD coverage in high‑dimensional spaces.
arXiv AI
Aug 24
Coverage-Driven Verification for Safety-by-Design in AI-Based Collision Avoidance Systems
The paper proposes a structured method for assessing the representativeness of Operational Design Domains (ODDs) in AI/ML-based aviation systems, focusing on safety assurance. It outlines a process flow from ODD definition to quantitative evaluation, recommending Kullback–Leibler divergence and Cramér’s V over chi‑squared tests for large datasets. The approach is illustrated with AI-based collision avoidance simulations, demonstrating how statistical distribution comparisons can support safety‑by‑design engineering aligned with EASA guidance.
arXiv AI
Aug 21
On the Applicability of Safety Nets: A Safety-By-Design Solution for Certifying Neural Networks
arXiv:2608. 20053v1 Announce Type: new Abstract: The integration of Artificial Intelligence (AI) in safety-critical aviation systems presents significant challenges for certification and deployment.
arXiv AI
Aug 6
Robust and Personalized Federated Learning for Aircraft-Engine Prognostics under Benign and Adversarial Client Heterogeneity
arXiv:2608. 04045v1 Announce Type: cross Abstract: Federated learning (FL) enables aircraft fleet operators to jointly train remaining-useful-life (RUL) models from engine sensor telemetry without sharing raw data.
Hugging Face Trending Papers
Aug 4
Robust and Personalized Federated Learning for Aircraft-Engine Prognostics under Benign and Adversarial Client Heterogeneity
Federated learning (FL) enables aircraft fleet operators to jointly train remaining-useful-life (RUL) models from engine sensor telemetry without sharing raw data. This study examines two complementary challenges: benign heterogeneity, where honest operators observe different operating conditions and fault modes, and adversarial heterogeneity, where compromised operators submit poisoned updates.
arXiv AI
Jul 2
From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems
arXiv:2410. 22526v2 Announce Type: replace Abstract: To effectively address potential harms from Artificial Intelligence (AI) systems, it is essential to identify and mitigate system-level hazards.