ABSTRACT:Project SUBVENTO is a joint research project of the research facility Technische Universität Braunschweig and the industry partner BBR -Baudis Bergmann Rösch Verkehrstechnik GmbH in close cooperation with the German Federal Agency for Technical Relief (THW). The Project is funded within the European Regional Development Fund (EFRE) allocated by the European Union. Therein an integrated system for the fast and automated remote detection of heat sources using an infrared camera is being developed. For a precise detection of fires or persons a very accurate navigation system including the algorithms and the hardware is required. Furthermore additional sensors like a multispectral camera applicable in small unmanned aerial vehicles (UAVs) should be implemented. Since the mentioned sensor equipment exceeds the current UAVs' payload limit, a new Carolo type aircraft with a wingspan of 3.6 m is designed. It has an increased payload capability and an extended flight time. This is possible due to an optimized aerodynamic layout and a high efficient propulsion system.

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Abstract:The main goal of the large-scale research facility FASCar ® are scienti c studies and analyses in the eld of driver assistance and vehicle automation. This includes also studies of human behavior, acceptance studies, test of new assistance systems and automation, as well as user friendliness. FASCar ® makes it possible to test and analyze innovative systems and developed functions in a simulated or even real tra c environment.

Vehicle trajectories from recorded video sequences-acquired by several contemporary methods of digital image processing-are compared with highprecision GPS data serving as a reference. The raw data has been created by driving some scenarios with a car equipped with several sensors, i.e. DGPS, acceleration sensor, etc. At the same time, the car was recorded by a video camera system in order to derive trajectory data by computer vision methods. Thus, the car is tracked by an Extended Kalman Filter (EKF) preceded by a background estimator. To improve the accuracy of the tracking data it is combined with a model-based approach for object detection. This approach fits a 3-dimensional wire frame model of the car into the image. The paper presents the driving scenarios of the car, the implemented image processing methods and a quantitative evaluation of the extracted trajectories obtained by two different image processing methods. Accuracy and precision of the methods are determined by comparing their results with the DGPS reference data of the car.