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Das übergeordnete Forschungsgebiet, in das sich die vorliegende Arbeit einbettet, befasst sich mit der Erforschung von informationsverabeitenden Prozessen im Gehirn und der Anwendung der resultierenden Erkenntnisse auf technische Systeme. In Analogie zu biologischen Systemen, deren Beschaffenheit aus den Anforderungen der Umwelt an ihr Verhalten resultiert, leitet sich die Anthropomorphie als Entwurfsprinzip für die Struktur des mit den Menschen interagierenden robotischen Assistenzsystemen ab. Der Autor behandelt in der vorliegende Arbeit das Problem der Erzeugung von Motorverhalten im dreidimensionalen Raum am Beispiel eines anthropomorphen Roboterarmes in einem anthropomorphen robotischen Assistenzsystem. Entwickelt wurde hierbei ein allgemeiner Ansatz, der die Konzepte der Erzeugung von Motorverhalten im 3D-Raum, der Voraussimulation dynamischer Systeme zur Systemdiagnose und zur Suche gewünschter Systemzustände, sowie ein Konzept der Organisation von Verhalten enthält und vereinigt. Nichtlineare dynamische Systeme bilden das mathematische Fundament, die einheitlich, formale Sprache des Ansatzes, mit der sowohl das Motorverhalten des Roboters als auch dessen zeitkontinuierliche Teilsysteme rückgekoppelt werden.
In this paper we describe an architecture for behavioral organization based on dynamical systems. This architecture
enables the generation of complex behavioral sequences, which is demonstrated using the example of approaching and
passing a door. The behavioral sequence is generated by activating and deactivating the elementary behaviors dependent
on sensory information and internal logical conditions. The architecture is demonstrated on a mobile KOALA robot and
in simulation as well.
In the presented work we compare machine learning techniques in the context of lane change behavior performed by humans in a semi-naturalistic simulated environment. We evaluate different learning approaches using differing feature combinations in order to identify appropriate feature, best feature combination, and the most appropriate machine learning technique for the described task. Based on the data acquired from human drivers in the traffic simulator NISYS TRS 1 , we trained a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able to predict lane changes up to 1.5 sec in beforehand.
The presented work formulates an framework in which early prediction of drivers lane change behavior is realized. We aim to build a representation of drivers lane change behavior in order to recognize and to predict driver's intentions as a first step towards a realistic driver model. In the test bed of the Institute of Neuroinformatik, based on the traffic simulator NISYS TRS 1, 10 individuals have driven in the experiments and they performed more then 150 lane change maneuvers. Lane-offset, distance to the front car and time to contact, were recorded. The acquired data was used to train - in parallel- a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able of performing a lane change prediction time of 1.5 sec beforehand. The proposed approach describes a framework for lane-change detection and prediction, which will serve as a prerequisite for a successful driver model.