The AI for Micro Mobility (μAFM) is the new research platform for investigation of novel active and passive electronic bicycle assistance control methods within the Department of Vehicle System Dynamics at the German Aerospace Center (DLR). Autonomous driving and artificial intelligence offer completely new possibilities for control systems. However, no studies have yet investigated the holistic approach, from the perception sensors to the control algorithms, with the aim of protecting vulnerable road users, particularly in the context of bicycles. The objective of our research is to contribute to this field of study. To this end, we equipped a production electric serial hybrid power train bicycle with a set of state-of-the-art sensors and computational hardware. The hardware and software integration of the various sensors as well as the network architecture is presented. Paving the path for further research in this field. Concluding, two real-word experiments are conducted to validate and investigate the performance of the setup for further control algorithms studies.

We introduce relaxed dot plots as an improvement of nonlinear dot plots for unit visualization. Our plots produce more faithful data representations and reduce moiré effects. Their contour is based on a customized kernel frequency estimation to match the shape of the distribution of underlying data values. Previous nonlinear layouts introduce column-centric nonlinear scaling of dot diameters for visualization of high-dynamic-range data with high peaks. We provide a mathematical approach to convert that column-centric scaling to our smooth envelope shape. This formalism allows us to use linear, root, and logarithmic scaling to find ideal dot sizes. Our method iteratively relaxes the dot layout for more correct and aesthetically pleasing results. To achieve this, we modified Lloyd's algorithm with additional constraints and heuristics. We evaluate the layouts of relaxed dot plots against a previously existing nonlinear variant and show that our algorithm produces less error regarding the underlying data while establishing the blue noise property that works against moiré effects. Further, we analyze the readability of our relaxed plots in three crowd-sourced experiments. The results indicate that our proposed technique surpasses traditional dot plots.

We introduce inverted stippling , a method to mimic an inversion technique used by artists when performing stippling. To this end, we extend Linde-Buzo-Gray (LBG) stippling to multi-class LBG (MLBG) stippling with multiple layers. MLBG stippling couples the layers stochastically to optimize for per-layer and overall blue-noise properties. We propose a stipple-based filling method to generate solid color backgrounds for inverting areas. Our experiments demonstrate the effectiveness of MLBG in terms of reducing overlapping and intensity accuracy. In addition, we showcase MLBG with color stippling and dynamic multi-class blue-noise sampling, which is possible due to its support for temporal coherence.

We present a framework for rapid prototyping of pervasive visual deficiency simulation in the context of graphical interfaces, virtual reality, and augmented reality. Our framework facilitates the emulation of various visual deficiencies for a wide range of applications, which allows users with normal vision to experience combinations of conditions such as myopia, hyperopia, presbyopia, cataract, nyctalopia, protanopia, deuteranopia, tritanopia, and achromatopsia. Our framework provides an infrastructure to encourage researchers to evaluate visualization and other display techniques regarding visual deficiencies, and opens up the field of visual disease simulation to a broader audience. The benefits of our framework are easy integration, configuration, fast prototyping, and portability to new emerging hardware. To demonstrate the applicability of our framework, we showcase a desktop application and an Android application that transform commodity hardware into glasses for visual deficiency simulation. We expect that this work promotes a greater understanding of visual impairments, leads to better product design for the visually impaired, and forms a basis for research to compensate for these impairments as everyday help.

This paper proposes a reactive obstacle avoidance approach based solely on image data from a monocular camera stream. By clustering and analyzing the optical flow, this approach is able to identify potential collisions with dynamic obstacles. Epipolar geometry is exploited to derive velocity commands that ensure a collision-free path for a highly maneuverable autonomous vehicle via a real-time optimizer. First, the underlying image processing and optimization principles are explained in detail, before simulation results show the general feasibility of the approach. Finally, real-world tests with the ROboMObil, the German Aerospace Center's robotic electric vehicle, are provided to demonstrate its applicability.

Selecting an embedded hardware platform for image processing has a big influence on the achievable performance. This paper reports our work on a performance benchmark of different implementations of some low-level vision algorithms. The algorithms are implemented on both Digital Signal Processor (DSP) and Field Programmable Gate Array (FPGA) high-speed embedded platforms. The target platforms are a TITMS320C6414 DSP and an Altera Stratix FPGA. The implementations are evaluated, compared and discussed. The DSP implementations outperform the FPGA implementations, but at the cost of spending all its resources to these tasks. FPGAs, however, are well suited to algorithms, which benefit from parallel execution.