This paper presents an ontology-supported approach to tackle the complexity of the Robustness Validation (RV) process of automotive electrical/electronic (E/E) components. The approach uses formalized...
arXiv:2608. 16421v1 Announce Type: new Abstract: This paper presents an ontology-supported approach to tackle the complexity of the Robustness Validation (RV) process of automotive electrical/electronic (E/E) components.
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:2606. 31131v1 Announce Type: new Abstract: To ensure safe on-road behavior, pre-deployment testing and failure discovery of Autonomous Driving Systems (ADS) is crucial.
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.
The Ontology-Based Contextual AI Evaluations (OB-CAIE) methodology introduces a structured approach to AI evaluation by defining clear testing coverage and balancing human expertise with automation. It employs two ontologies—the Domain‑Specific Ontology (DSO) outlining what is tested, and the Evaluation Process Ontology (EPO) detailing how it is tested—to create a tractable problem space that can be applied to single or multiple AI evaluations. OB‑CAIE enables traceable, visualizable failure points and incorporates human judgment at scientifically grounded junctures where machine input is insufficient.
Teach-to-Crash is a closed‑loop testing framework that uses a dual‑LLM architecture to generate collision‑inducing scenarios for autonomous driving systems. A high‑reasoning Teacher LLM controls the search when collision metrics stagnate, while a low‑reasoning Student LLM produces simulator‑executable scenarios in JSON. In a CARLA case study, Teach‑to‑Crash achieved the highest collision hit rate (90.79 %), the shortest mean time‑to‑collision (18.31 s), and superior diversity and avoidability metrics compared to other methods.
arXiv:2601.22118v3 Announce Type: replace Abstract: Artificial Intelligence (AI) has been on the rise in many domains, including numerous safety-critical applications. However, for complex systems in...
arXiv:2607. 14826v1 Announce Type: cross Abstract: Safe physical AI for robot actions are required not only likely to succeed but tested to be safe before execution.
arXiv:2607. 18262v1 Announce Type: new Abstract: The SFB 1574 Circular Factory is building a shared knowledge graph infrastructure for integrating data about returned products.
arXiv:2609.25945v1 Announce Type: new Abstract: The field of Artificial Intelligence has been adopted for many application domains. Vision Language Models are one of the recently advanced AI techniqu...
arXiv:2607. 17963v1 Announce Type: new Abstract: Ontology extension refers to the process of enriching an existing ontology in response to emerging requirements, making it more complete.