← Back to all news
arXiv AI By Thomas Stefani, Johann Maximilian Christensen, Elena Hoemann, Frank K\"oster, Sven Hallerbach

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

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

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.

By Thomas Stefani, Johann Maximilian Christensen, Elena Hoemann, Frank K\"oster, Sven Hallerbach
safety
More like this →
arXiv AI
Sep 4

FLY-EVAL++: An Evidence-Driven Evaluation Protocol for Safety-Constrained Flight Prediction with Large Language Models

FLY-EVAL++ is an evidence-driven evaluation protocol designed for safety-constrained flight prediction with large language models. It combines deterministic verification of protocol compliance, physical feasibility, and safety constraints with rubric-guided aggregation into interpretable multi-dimensional scores. Applied to Flight Trajectory and Attitude Prediction, the protocol revealed that safety compliance is the most discriminative dimension among 66 LLMs, with models showing up to 28-point differences in safety scores and recurrent failures such as safety violations under physically plausible predictions and instability in multi-step rollouts.

By Yalun Wu, Junfeng Fang, Jiawei Wang, Haotian Liu, Qijun Yang, Minghan Yang, Hongcheng Guo, Zhoujun Li, Boyang Wang
llmssafety
More like this →
arXiv AI
5d ago

Toward a Decision-Assurance Layer for AI-Assisted Flight Planning in Air Traffic Management

arXiv:2609.13552v1 Announce Type: new Abstract: Generative AI is increasingly being used informally in Air Traffic Management (ATM) for tasks such as flight plan generation, trajectory interpretation...

By Alexandre Barreto (George Mason University), Shou Matsumoto (George Mason University), Jorge Valverde-Rebaza (Tecnol\'ogico de Monterrey), Cleiton Ataide (DECEA: Department of Airspace Control), Paulo Costa (George Mason University)
safety
More like this →