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PublicationPioneering Situation-aware Autonomy and Safety for Off-Highway Machinery in Unpredictable Terrain( 2024-08)A limiting factor for the large-scale deployment of autonomous off-road machinery is the lack of reliability and proof of safety. Assuring these aspects is not just complex but hard, and remains an open research topic: Off-road domains are frequently changing, and the overall variability prevents the consideration of every important aspect of the environment or situation during the development phase. In contrast, humans are extremely capable of adapting to unforeseen events and acting safely under such uncertainty. A common strategy is therefore to adopt biology-inspired methods, like implementing human-like cognition and reasoning, to maintain safety.
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PublicationTangible Ecosystem Design( 2024-02-01)
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PublicationHow do Platforms Make Money?( 2024-02)
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PublicationOpportunities and Limitations of AI in Human-Centered Design a Research Preview( 2024)
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PublicationA Taxonomy for Platform Revenue Models: An Empirical-to-Conceptual Development Approach( 2024)
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PublicationAnrufgründe bei medizinischen Notfällen( 2024)Hintergrund: Rettungsleitstellen sehen sich mit steigenden Herausforderungen durch kontinuierlich steigende Notrufzahlen konfrontiert. Zur besseren Strukturierung und Priorisierung der Notrufgespräche werden vielerorts standardisierte Abfragesysteme implementiert. Aktuelle Entwicklungen im Bereich der künstlichen Intelligenz eröffnen neue Möglichkeiten der Entscheidungsunterstützung von Disponierenden. Voraussetzung hierfür ist ein prozesshaftes Modell des Notrufdialogs. Ziel der Arbeit: Auf Basis einer Analyse der komplexen Anrufgründe von Notrufgesprächen wird deren übergreifende Struktur abgeleitet und modellhaft dargestellt. Material und Methoden: 50 randomisiert ausgewählte Aufzeichnungen medizinischer Notrufe einer integrierten Rettungsleitstelle aus dem Jahr 2022 wurden transkribiert, unter Anwendung einer qualitativen Inhaltsanalyse induktiv codiert, kategorisiert und in ein prozesshaftes Modell des Notrufdialogs überführt. Ergebnisse: Das typische Notrufgespräch besteht aus zwei konsekutiven Prozessen, einem Einstiegs- und einem Rückfrageprozess. Der Einstiegsprozess dient der Informationsgewinnung mit dem Ziel, abschätzen zu können, ob es sich beim Notrufgrund um ein einzelnes Hauptproblem, die Kombination aus mehreren Gesundheitsbeeinträchtigungen oder ein Ereignis vor bzw. nach einer Gesundheitsbeeinträchtigung handelt. Der Rückfrageprozess dient der Präzisierung der Zustands- bzw. Problembeschreibung. Diskussion: Auf Basis der zufällig ausgewählten Notrufgespräche konnte der idealtypische Ablauf des Notrufgesprächs abgeleitet und in einem phasen- bzw. prozesshaften Modell dargestellt werden, auf dessen Grundlage nun KI-gestützte Notrufabfragesysteme entwickelt werden können.
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PublicationBridging the Gap Between IDS and Industry 4.0 - Lessons Learned and Recommendations for the Future( 2024)The Plattform Industrie 4.0 (PI4.0) and the International Data Spaces Association (IDSA) are two independent, parallel initiatives with clear focuses. While PI4.0 addresses communication and interaction between networked assets in a smart factory and/or supply chain across an asset or product lifecycle, IDSA is about a secure, sovereign system of data sharing in which all stakeholders can realize the full value of their data. Since data sharing between companies requires both interoperability and data sovereignty, the question emerges regarding the feasibility and rationality of integrating the expertise of PI4.0 and IDSA. The IDS-Industrial Community (IDS-I) is an extension of IDSA whose goal is to strengthen the cooperation between IDSA and PI4.0. Two fields of expertise could be combined: The Platform's know-how in the area of Industrie 4.0 (I4.0) and the IDSA's expertise in the areas of data sharing ecosystems and data sovereignty. In order to realize this vision, many aspects have to be taken into account, as there are discrepancies on multiple levels. Specifically, at the reference architecture level, we have the RAMI4.0 model on the PI4.0 side and the IDS Reference Architecture Model (IDS-RAM) on the IDSA side. While the existing I4.0 and IDS specifications are incompatible e.g. in terms of models (i.e., the AAS metamodel and the IDS information model) and APIs, there is also the issue of interoperability between I4.0 and IDS solutions. This position paper aims to bridge the gap between IDS and PI4.0 by not only analyzing how their existing concepts, tools, etc. have been "connected" in different contexts. Rather, this position paper makes recommendations on how different technologies could be combined in a generic way, independent of the concrete implementation of IDS and/or I4.0 relevant technology components. This paper could be used by both the IDS and I4.0 communities to further improve their specifications, which are still under development. The lessons learned and feedback from the initial joint use of technology components from both areas could provide concrete guidance on necessary improvements that could further strengthen or extend the specifications. Furthermore, it could help to promote the IDS architecture and specifications in the industrial production and smart manufacturing community and extend typical PI4.0 use cases to include data sovereignty by incorporating IDS aspects.
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PublicationRisk Management Core - Toward an Explicit Representation of Risk in Automated Driving( 2024)While current automotive safety standards provide implicit guidance on how unreasonable risk can be avoided, manufacturers are required to specify risk acceptance criteria for Automated Driving Systems (SAE Level 3 and higher). However, the 'unreasonable' level of risk of Automated Driving Systems is not yet concisely defined. Solely applying current safety standards to such novel systems could potentially not be sufficient for their acceptance. As risk is managed with implicit knowledge about safety measures in existing automotive standards, an explicit alignment with risk acceptance criteria is challenging. Hence, we propose an approach for an explicit representation and management of risk, which we call the Risk Management Core. The proposal of this process framework is based on requirements elicited from current safety standards and is applied to the task of specifying safe behavior for an Automated Driving System in an example scenario.
