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Our university carries out various research projects. Among others, the project Schluckspecht is an interdisciplinary work on different ultra-efficient car concepts for international contests. Besides the engineering work, one part of the project deals with real-time data visualization. In order to increase the efficiency of the vehicle, an online monitoring of the runtime parameters is necessary. The driving parameters of the vehicle are transmitted to a processing station via a wireless network connection. We plan to use an augmented reality (AR) application to visualize different data on top of the view of the real car. By utilizing a mobile Android or iOS device a user can interactively view various real-time and statistical data. The car and its components are meant to be augmented by various additional information, whereby that information should appear at the correct position of the components. An engine e.g. could show the current rpm and consumption values. A battery on the other hand could show the current charge level. The goal of this paper is to evaluate different possible approaches, their suitability and to expand our application to other projects at our university.
Walking interfaces offer advantages in navigation of VE systems over other types of locomotion. However, VR helmets have the disadvantage that users cannot see their immediate surroundings. Our publication describes the prototypical implementation of a virtual environment (VE) system, capable of detecting possible obstacles using an RGB-D sensor. In order to warn users of potential collisions with real objects while they are moving throughout the VE tracking area, we designed 4 different visual warning metaphors: Placeholder, Rubber Band, Color Indicator and Arrow. A small pilot study was carried out in which the participants had to solve a simple task and avoid any arbitrarily placed physical obstacles when crossing the virtual scene. Our results show that the Placeholder metaphor (in this case: trees), compared to the other variants, seems to be best suited for the correct estimation of the position of obstacles and in terms of the ability to evade them.