Hans‐Jörg Vögel

Digitalization in automotive and industrial systems

Autonomous systems are an important part of todays and future solutions for the automotive and industrial sector. The research and development activities to enable high/full automated driving and industry 4.0 have to deal with a lot of new requirements (e.g. fail operational, cyber security), technologies (connectivity over 5G, neuronal networks, future computing platforms) and topics (data analytics, artificial intelligence). Furthermore processes, methods und tools lack behind and need to speed up to cope with all the consequences in validation and verification. The short paper will give an overview over these challenges and the actual state of research and the development in the field of digital autonomous systems.

Emotion-awareness for intelligent vehicle assistants: a research agenda

EVA is describing a new class of emotion-aware autonomous systems delivering intelligent personal assistant functionalities. EVA requires a multi-disciplinary approach, combining a number of critical building blocks into a cybernetics systems/software architecture: emotion aware systems and algorithms, multimodal interaction design, cognitive modelling, decision making and recommender systems, emotion sensing as feedback for learning, and distributed (edge) computing delivering cognitive services.

Diversity Protected, Cache Based Reliable Content Distribution Building on Scalable, P2P, and Multicast Based Content Discovery

This paper presents a new scalable distributed architecture of collaborating caches. Our approach masks faults in content distribution scenarios towards clients. Basically, in case of a failure, our architecture requests content from distributed caches. When the content is found it is transparently transported to the requesting receiver. Distributed architectures of collaborating caches for fault tolerant content delivery pose a fundamental challenge: The probability to find requested content stored in caches, rises with the number of caches involved. A rising number of caches however implicates more signaling overhead for each query leading to scalability problems. In this work we present a new reliable distributed content discovery architecture that solves the scalability problem. Building on a 2 tier Peer-to-Peer overlay networks in combination with local summary distribution based on Multicast signaling, we provide a fast and scalable signaling architecture for content discovery in large distributed cache networks.