Vehicle guidance in complex scenarios such as inner-city traffic requires an in-depth understanding of the current situation. In order to select the appropriate behavior for an autonomous vehicle, an analysis of the situation is needed. The analysis consists of an estimation of the situation's development with respect to the selected behavior. This can only be done using higher-level reasoning techniques. In this paper, an approach for situation interpretation for autonomous vehicles is presented. The approach relies on case-based reasoning in order to predict the evolvement of the current situation and to select the appropriate behavior. Case-based reasoning allows to utilize prior experiences in the task of situation assessment.

Human activity recognition is an essential ability for service robots and other robotic systems which are in interaction with human beings. To be proactive, the system must be able to evaluate the current state of the user it is dealing with. Also future surveillance systems will benefit from robust activity recognition if realtime constraints are met, allowing to automate tasks that have to be fulfilled by humans yet. In this paper, a thorough analysis of features and classifiers aimed at human activity recognition is presented. Based on a set of 10 activities, the use of different feature selection algorithms is evaluated, as well as the results different classifiers (SVMs, Neural Networks, Bayesian Classifiers) provide in this context. Also the interdependency between feature selection method and chosen classifier is investigated. Furthermore, the optimal number of features to be used for an activity is examined.

This paper proposes a tracking system called VooDoo for 3D tracking of human body movements based on a 3D body model and the iterative closest point (ICP) algorithm. The proposed approach is able to incorporate raw data from different input sensors, as well as results from feature trackers in 2D or 3D. All input data is processed within the same model fitting step by modeling all input measurements in 3D model space. The system has been implemented and runs in realtime at appr. 10-14 Hz. Experiments with complex human movements exhibit the characteristics and advantages of the proposed approach

This paper presents the distributed robot control software architecture developed for the autonomous service robot Albert2. The development of this architecture is focused on two major issues: modularity and the integration of learning aspects. Each module within the architecture is presented, as well as the underlying event-based communication framework. An approach for integration of learning capabilities is proposed.