Experimental vehicles FASCar®-II and FASCar®-E

The main goal of the large-scale research facility FASCar® are scientific studies and analyses in the field 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 traffic environment

AIM in-vehicle platform for ITS services

The application platform for intelligent mobility (AIM) is a large scale research infrastructure operated by the Institute of Transportation System of the German Aerospace Center (DLR) in the city and region of Braunschweig. The in-vehicle platform for ITS services (ITS, Intelligent Transportation Systems) is an integral part of this large-scale research facility. The in-vehicle platform for ITS services can be seen as a modular kit which enables up to 50 vehicles to take part in a Vehicle-to-Vehicle and Vehicle-to-Infrastructure (V2X) communications in test sites like the V2X reference track in the city of Braunschweig. The in-vehicle platform for ITS services along with its integration into the AIM test field provides answers to a broad set of research questions in the Field of V2X communications on public roads. For example effects can be analyzed, which take place when vehicles with mixed equipped communication technologies are sharing one road

A Comparison of Trajectories and Vehicle Dynamics Acquired by High Precision GPS and Contemporary Methods of Digital Image Processing

Vehicle trajectories from recorded video sequences—acquired by several contemporary methods of digital image processing—are compared with highprecision GPS data serving as 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. For improving the accuracy of the tracking data it is combined with a model-based approach for object detection. This approach �ts 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 di�erent image processing methods. Accuracy and precision of the methods are determined by comparing their results with the DGPS reference data of the car

Kooperative Automation zur Längsführung im urbanen Straßenverkehr

Die Automatisierung der Fahraufgabe kann dazu beitragen, das Fahren sicherer, effizienter und komfortabler zu gestalten. Für verhältnismäßig strukturierte Umgebungen wie Autobahnen ist diese Entwicklung bereits weit fortgeschritten. Adaptive Geschwindigkeitsregelung (ACC), Spurhalteassistent und andere Fahrerassistenzsysteme entlasten den Fahrer dort bereits heute. Die Markteinführung erster Systeme zum hochautomatisierten Fahren auf der Autobahn wird ab 2020 erwartet . Im urbanen Umfeld ist das Verkehrsgeschehen jedoch deutlich komplexer und unstrukturierter. Im Rahmen des Projekts Next Generation Car (NGC) verfolgt das Institut für Verkehrssystemtechnik des DLR mit dem System urbanDRIVE das Ziel einer kooperativen Automation zur längs- und quergeführten Fahrt im urbanen Straßenraum. Als Baustein davon wurde mit urbanDRIVE 1.0 eine Automationsfunktion zur Längsführung entwickelt, prototypisch umgesetzt und im Testfeld AIM im öffentlichen Verkehr in Braunschweig getestet. Der Beitrag erläutert dieses System