activity
20192022
most citedDEIS: Dependability Engineering Innovation for Industrial CPS

7 citations · 13 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SE2022

Assurance Cases as Foundation Stone for Auditing AI-enabled and Autonomous Systems: Workshop Results and Political Recommendations for Action from the ExamAI Project

Rasmus Adler, Michael Klaes

The European Machinery Directive and related harmonized standards do consider that software is used to generate safety-relevant behavior of the machinery but do not consider all ki…

cs.SE2022★ 1 cited

Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception

Janek Groß, Rasmus Adler, Michael Kläs +3

Data-driven models (DDM) based on machine learning and other AI techniques play an important role in the perception of increasingly autonomous systems. Due to the merely implicit d…

cs.SE2022★ 2 cited

Towards a Roadmap for Trustworthy Dynamic Systems-of-Systems

Rasmus Adler, Frank Elberzhager, Julien Siebert

This paper gives insights into the DynaSoS project, which aims to propose a cross-domain roadmap for systems engineering research into trustworthy dynamic systems-of-systems with a…

cs.AI2022

Integrating Testing and Operation-related Quantitative Evidences in Assurance Cases to Argue Safety of Data-Driven AI/ML Components

Michael Kläs, Lisa Jöckel, Rasmus Adler +1

In the future, AI will increasingly find its way into systems that can potentially cause physical harm to humans. For such safety-critical systems, it must be demonstrated that the…

cs.SE2021★ 7 cited

DEIS: Dependability Engineering Innovation for Industrial CPS

Erik Armengaud, Georg Macher, Alexander Massoner +15

The open and cooperative nature of Cyber-Physical Systems (CPS) poses new challenges in assuring dependability. The DEIS project (Dependability Engineering Innovation for automotiv…

cs.CY2019★ 3 cited

Hardening of Artificial Neural Networks for Use in Safety-Critical Applications -- A Mapping Study

Rasmus Adler, Mohammed Naveed Akram, Pascal Bauer +6

Context: Across different domains, Artificial Neural Networks (ANNs) are used more and more in safety-critical applications in which erroneous outputs of such ANN can have catastro…