Remote sensing satellites are playing very important roles in diverse earth observation fields. However, long revisit period, high cost and dense cloud cover have been the main limitations of satel...

Volunteered remote sensing data generation with air passengers as sensors

Today, driver assistance systems and autonomous vehicles are actively developed and verified in real-world test scenarios. These real-world tests are neither cheap nor sufficient to ensure a reasonably low enough risk of failure. In order to enable efficient and methodical system tests, we propose the use of Virtual Testbeds (VTBs) as a software environment in which "Digital Twins" of real vehicles and road scenarios are modelled and simulated. In this contribution, we describe a rule-based approach and overall workflow for semi-automatic generation of road scenarios for the automated testing of driver assistance systems. Furthermore, a combination of existing procedural generation techniques suited to this application is selected to automatically generate scenes which adequately mimic real-world scenarios.

Automated generation of virtual road scenarios for efficient tests of driver assistance systems

Our objective is to simulate entire cities with the most realistic possible scenario. This kind of systems require a lot of processing power, therefore we use hybrid computer clusters with graphics cards (GPUs). GPUs allow us to accelerate calculations and visualization.

Data-driven crowd simulation

We present a city reconstruction and visualization framework that integrates geometric models reconstructed with a range of different techniques. The framework generates the vast majority of buildings procedurally, which yields plausible visualizations for structurally simple buildings, e.g. residential buildings. For structurally complex landmarks, e.g. churches, a procedural approach does not achieve satisfactory visual fidelity. Thus, we also employ image-based techniques to reconstruct the latter in a more realistic, recognizable way. As the manual acquisition of data required for the procedural and image-based reconstructions is practically infeasible for whole cities, we rely on publicly available data as well as crowd sourcing projects. This enables our framework to render views from cities without any dedicated data acquisition as long as there are sufficient public data sources available. To obtain a more lively impression of a city, we also visualize dynamic features like weather conditions and traffic based on publicly available real-time data.

City reconstruction and visualization from public data sources

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City reconstruction and visualization from public data sources