Mobile navigation apps are among the most used mobile applications and are often used as a baseline to evaluate new mobile navigation technologies in field studies. As field studies often introduce external factors that are hard to control for, we investigate how pedestrian navigation methods can be evaluated in virtual reality (VR). We present a study comparing navigation methods in real life (RL) and VR to evaluate if VR environments are a viable alternative to RL environments when it comes to testing these. In a series of studies, participants navigated a real and a virtual environment using a paper map and a navigation app on a smartphone. We measured the differences in navigation performance, task load and spatial knowledge acquisition between RL and VR.From these we formulate guidelines for the improvement of pedestrian navigation systems in VR like improved legibility for small screen devices. We furthermore discuss appropriate low-cost and low-space VR-locomotion techniques and discuss more controllable locomotion techniques.

In the transfer from fossil fuels to renewable energies, grid operators, companies and farms develop an increasing interest in smart energy management systems which can reduce their energy expenses. This requires sufficiently detailed models of the underlying components and forecasts of generation and consumption over future time horizons. In this work, it is investigated via a real-world case study how data-based methods based on regression and clustering can be applied to this task, such that potentially extensive effort for physical modeling can be decreased. Models and automated update mechanisms are derived from measurement data for a photovoltaic plant, a heat pump, a battery storage, and a washing machine. A smart energy system is realized in a real household to exploit the resulting models for minimizing energy expenses via optimization of self-consumption. Experimental data are presented that illustrate the models' performance in the real-world system. The study concludes that it is possible to build a smart adaptive forecast-based energy management system without expert knowledge of detailed physics of system components, but special care must be taken in several aspects of system design to avoid undesired effects which decrease the overall system performance.Energies 2020, 13, 2084 2 of 42 to exploit these installations by increasing self-consumption, i.e., maximizing the amount of locally produced energy that is directly used.However, to perform such a task in an automated way, smart energy systems are needed which are able to determine and execute optimal operation strategies by rescheduling controllable loads and control of storage devices in accordance with the expected energy production profile. In simple system setups such as integrated PV-storage systems, relay-based self-consumption maximization strategies might suffice and are commonly found in practice [6]. For more complex system setups with several controllable devices, in many cases, more advanced optimization strategies are proposed, which require sufficiently detailed models of system components [7]. In the case of a large office building, a farm, or an industrial premise, loads and devices exhibiting very specific or complex consumption behaviors might prohibit the usage of advanced optimization methods due to the effort of deriving and implementing the necessary white or gray box models. This situation was reported by Žáčeková et al. [8] and Sturzenegger et al. [9] for the usage of model predictive control (MPC) in the minimization of the energy demand of office buildings. Furthermore, the application of forecast methods for the expected power generation is investigated in several studies, indicating financial benefits for a smart energy system or its integration into the overall power system (e.g., [6,10,11]). If these benefits are to be exploited in a smart energy system, forecast models for volatile generation and consumption must also be developed, increasing the initial modeling effort even further.In the literature, there are basi...

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Bottom-up saliency models identify the salient regions of an image based on features such as color, intensity and orientation. These models are typically used as predictors of human visual behavior and for computer vision tasks. In this paper, we conduct a systematic evaluation of the saliency maps computed with four selected bottom-up models on images of urban and highway traffic scenes. Saliency both over whole images and on object level is investigated and elaborated in terms of the energy and the entropy of the saliency maps. We identify significant differences with respect to the amount, size and shape-complexity of the salient areas computed by different models. Based on these findings, we analyze the likelihood that object instances fall within the salient areas of an image and investigate the agreement between the segments of traffic participants and the saliency maps of the different models. The overall and object-level analysis provides insights on the distinctive features of salient areas identified by different models, which can be used as selection criteria for prospective applications in autonomous driving such as object detection and tracking.

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