AbsWd-In this project a system for realtime airborne areal traffic monitoring has b e n designed. implemented and demonstrated in Berlin.The system encompnues the following steps: image and attitude recording aboard an aircraft, image preprocessing and transfer to ground, further image processing for extracting the traffic data and aggregation of traffic data and their input into newly developed simulation and prognosis tools to close the gaps between overtlights. The system is completed by an open interface to service companies that support end nsen with traffic information and recommendations.The technological challenges were on the software side the development of appropriate fast image processing methods and new traffic simulation and prognosis tools and on the hardware side the integration of all components and subsystems to a working complete system. A major aim of the project was the comparison of the new approach with established methods of traffie monitoring w.r.t accuracy and effieiancy.For validating the complete LUMOS concept and its implcmrntstion a flight campaign wps prrfwmrd by the DLRlnstihlle of Transport Research and the other project partners in the area of Berlin in May 2003.First resultr ofcomparison of airborne and conventional traffic measurements are presented in this paper.Besides DLR, the following partner collaborate within

Autonomous vehicles and robots require increasingly more robustness and reliability to meet the demands of modern tasks. These requirements specially apply to cameras because they are the predominant sensors to acquire information about the environment and support actions. A camera must maintain proper functionality and take automatic countermeasures if necessary. However, there is little work that examines the practical use of a general condition monitoring approach for cameras and designs countermeasures in the context of an envisaged high-level application. We propose a generic and interpretable self-health-maintenance framework for cameras based on dataand physically-grounded models. To this end, we determine two reliable, real-time capable estimators for typical image effects of a camera in poor condition (defocus blur, motion blur, different noise phenomena and most common combinations) by comparing traditional and retrained machine learning-based approaches in extensive experiments. Furthermore, we demonstrate how one can adjust the camera parameters (e.g., exposure time and ISO gain) to achieve optimal whole-system capability based on experimental (non-linear and non-monotonic) input-output performance curves, using object detection, motion blur and sensor noise as examples. Our framework not only provides a practical ready-to-use solution to evaluate and maintain the health of cameras, but can also serve as a basis for extensions to tackle more sophisticated problems that combine additional data sources (e.g., sensor or environment parameters) empirically in order to attain fully reliable and robust machines.

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