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.

A common approach to social distancing in robot navigation are spatial cost functions around humans that cause the robot to prefer paths that do not come too close to humans. However, in unpredictably dynamic scenarios, following such paths may produce robot behavior that appears confused. The concept of directional costs in cost functions [9] is supposed to alleviate this problem without incurring the problem of combinatorial explosions using temporal planning. With directional cost functions, a robot attempts to solve spatial conflicts by adjusting the velocity instead of the path, where possible. To complement results from simulations, in this paper we describe a user study we conducted with a PR2 robot and human participants to evaluate the new cost function type. The study shows that the real robot behavior is similar to the observations in simulation, and that participants rate the robot behavior less confusing with the adapted cost model. The study also shows other important behavior cues that can influence motion legibility.

With the work at hand we investigate the legibility factors goal-predictability and trajectory-predictability in a human-robot path crossing scenario and their correlation with other HRI properties like safety, comfort, and reliability in order to assess which factor is more important for a safe and comfortable interaction.

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 future robots will more and more enter our daily life. If we want to increase their acceptance it is necessary that people feel safe in the surrounding of robots. As a prerequisite we think that the robot's behavior has to be legible in order to achieve such a feeling of perceived safety. With our present experiment we assess the perceived safety participants feel when an autonomous robot is crossing their path. Therefore participants are presented with a video based scenario in first person perspective. The robot is moving with two different navigation algorithms which allows us to test whether the legibility has an influence on the perceived safety and whether the two navigation algorithms differ regarding their resulting legibility and thus perceived safety. Results show that legibility as defined here increases perceived safety of both navigation methods while the level of perceived safety differs between them.

Robot path planning has traditionally concentrated on collision-free paths. For robots that collaborate closely with humans, however, the situation is different in two respects: 1) the humans in the robot's environment are not randomly moving objects, but cognitive beings who can deliberately make way for a robot to pass and 2) the quality of a navigation plan depends less on quantitative efficiency criteria, but rather on the acceptance of humans. In this paper, we introduce a robot navigation approach that takes into account human-centered requirements and the collaborative nature of the interaction between the human and the robot.