For digital mobility assistants it is advantageous to know users' mobility habits to be able to infer the most probable departure time and next destination. Different approaches are known to face this challenge, but most of them either have a very static feature model and limited extensibility capabilities or they are very complex and require exponential amount of training data for every added feature. This paper introduces a flexible and extendible mobility model - to represent a user's movement and habits - using a Variable-order Markov Model (VOMM) based on users' mobility patterns enriched with different temporal context information. Since this model uses a tree like data structure, it is possible to find patterns of different lengths in the same training data. Spatio-temporal next location prediction is based on the Prediction by Partial Matching (PPM) algorithm. We examine several classification and regression based machine learning algorithms for probability fusion of next location candidates and possible departure times to obtain the most accurate joint probability for the predicted location. The resulting prediction accuracy is between 60% and 81%.

In order to share knowledge about road situations vehicle-to-vehicle (V2V) communication is used.Autonomous driving vehicles are able to drive and park themself without driver interactions or presence, but are still inefficient about the drivers needs as they don't anticipate the users' behaviour.For instance, if a user wants to stop for quick grocery shopping, there is no need looking for long term parking in far distance, a short-term parking zone near the grocery shop would be adequate.To enable autonomous cars to make such decisions, they could benefit from awareness of their drivers' context.Knowledge about a users' activities and position may help to retrieve context information.To be able to describe the meaning of a visited place for user, we introduce a variant of semantic place labeling based on various sensor data.Data sourced by, e.g.smartphones or vehicles, is taken into account for gathering personalized context information, including Bluetooth, motion activity, status data and WLAN, and also to compensate for potential inaccuracies.For the classification of place types, over 80 features are generated for each stop.Thereby, geographic data is enriched with point of interest (POI)-information from different location-based context providers.In our experiments, we classify semantic categories of locations using parameter optimized multi-class and smart binary classifiers.An overall accuracy of 88.55% correctly classified stops is achieved using END classifier.A classification without GPS data yields an accuracy of 85.37%, demonstrating that alternative smartphone data can largely compensate for inaccurate localizations based on the fact of 88.55% accuracy, where GPS data was used.Knowing the semantics of a location, the provided context can be used to further personalize autonomous vehicles.

Over the last two years, the Modular OpenRobots Simulation Engine (MORSE) project1 went from a simple extension plugged on the Blender's Game Engine to a full-fledged simulation environment for academic robotics. Driven by the requirements of several of its developers, tools dedicated to Human-Robot interaction simulation have taken a prominent place in the project. This late breaking report discusses some of the recent additions in this domain, including the immersive experience provided by the integration of the Kinect device as input controller. We also give an overview of the experiences we plan to complete in the coming months.

Service robots that work together with humans in domestic and constantly changing environments should have a general understanding about their human partners and the tasks that are to be performed. This would enable them to verify their beliefs about the common tasks and the goals of their human partners and detect unexpected events and failures. In this paper we present a way of acquiring general, spatio-temporal plan representations from human motion tracking data in different environments. Using an annotated data set for table setting tasks in a typical kitchen environment, we first cluster the static positions of the participants and create a spatial model relative to furniture objects that are given by a semantic map and linked to a knowledge base. Based on this spatial model learned in one kitchen, we automatically generate spatio-temporal plan representations in different kitchen environments with known semantic maps. We show that our models can successfully be used to give a robot a basic understanding about a task executed by a human in three different environments. We evaluate the quality of our automatic generation of the plan representations and present an example application of plan supervision using a learned model from one kitchen to differentiate tasks performed by humans in other kitchens.

In the past, there has been a tremendous advance in the area of simultaneous localization and mapping (SLAM). However, there are relatively few approaches for incorporating prior information or knowledge about structural similarities into the mapping process. Consider, for example, office buildings in which most of the offices have an identical geometric layout. The same typically holds for the individual stories of buildings. In this paper, we propose an approach for generating alignment constraints between different floors of the same building in the context of graph-based SLAM. This is done under the assumption that the individual floors of a building share at least some structural properties. To identify such areas, we apply a particle filter-based localization approach using maps and observations from different floors. We evaluate our system using several real datasets as well as in simulation. The results demonstrate that our approach is able to correctly align multiple floors and allows the robot to generate consistent models of multi-story buildings.