Higher levels of vehicle automation come with new challenges for designing safe systems. The Human Machine-Interface (HMI) plays a key role in mediating the interaction between the human driver and vehicle automation. By providing the driver with appropriate feedback, the HMI has the potential to increase mode awareness and situational awareness. For the development of appropriate HMI solutions, usability assessments are essential. Immersive Virtual Reality (VR) technology enables researchers and designers to construct realistic virtual prototypes and immersive evaluation scenarios with less time and resources. The current study presents a VR evaluation tool called VRHEAD, which is designed to facilitate an iterative design process and support the rapid implementation of virtual prototypes to evaluate of an automated vehicle’s HMI. Initial results indicate that VRHEAD is a promising approach for the rapid implementation and evaluation of design concepts. The use of VR tools, like VRHEAD, can reduce the time and costs associated with developing high-fidelity prototypes and provide more flexibility in modifying a design according to new research findings, thus broadening the exploration of the HMI design space.

The project “Factory-in-a-day” aims at reducing the installation time of a new hybrid robot-human production line, from weeks or months that current industrial systems now take, down to one day. The ability to rapidly install (and reconfigure) production lines where robots work alongside humans will strongly reduce operating cost and open a range of new opportunities for industry. In this paper, we explore a method of collaborative fabrication planning with the help of Augmented Reality as part of the concept Augmented Fabrication. In order to plan a new production line, two co-located workers at the factory wear a Microsoft Hololens head-mounted display and thus share a common visual context on the planed position of the robots and the production machines. They are assisted by an external remote expert connected via the Internet who is virtually co-located. We developed three different visualizations of the state of the local collaboration and plan to compare them in a user study.

Remote maintenance of industrial manipulators often is performed via telephone support. Recent approaches in the context of the 'Industry 4.0' consider internet technologies and Augmented Reality (AR) to enhance situation awareness between external experts and local service technicians. We present two AR-based case studies: First, a mobile AR architecture based on optical see through glasses is used for an on-site local repair task. Second, a remote architecture based on a portable tablet PC and a high precision tracking system is used to realize an off-site expert access. The to-be-serviced machine is visualized inside of a large area similar to a machinery hall and can be inspected by the experts walking around this virtual plant using the tablet and perspectively correct rendering to understand the production process and the operation context. Both methods have been evaluated in first user studies.

Data centers are increasingly becoming larger and dynamic due to virtualization. In order to leverage the performance modeling and prediction techniques, such as Palladio Component Model or Descartes Modeling Language, in such a dynamic environments, it is necessary to automate the model extraction. Building and maintaining such models manually is not feasible anymore due to their size and the level of details. This paper is focused on traffic models that are an essential part of network infrastructure. Our goal is to decompose real traffic dumps into models suitable for performance prediction using Descartes Network Infrastructure modeling approach. The main challenge was to efficiently encode an arbitrary signal in the form of simple traffic generators while maintaining the shape of the original signal. We show that a typical 15 minute long tcpdump trace can be compressed to 0.4-15% of its original size whereas the relative median of extraction error is close to 0% for the most of the 69 examined traces.