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Oldenburg, Germany

Asif A.,University of Oldenburg | Heuten W.,OFFIS | Boll S.,University of Oldenburg
NordiCHI 2010: Extending Boundaries - Proceedings of the 6th Nordic Conference on Human-Computer Interaction

Visual and auditory displays successfully complement each other presenting information in car navigation systems. However, they distract the visual and auditory attention of the driver, which is needed in many primary driving tasks, such as maneuvering the car or observing the traffic. Tactile interfaces can form an alternative way to display spatial information. The way of how exactly information should be presented in a vibro-tactile way is explored rarely. In this paper we investigate three different designs of vibro-tactile stimulation to convey distance information to the driver using a tactile waist belt. We explore the tactile parameters intensity, rhythm, duration, and body location for encoding the distance information. We conduct a comparative experiment on a real navigation scenario in an urban environment to evaluate our designs. In our study we discovered that rhythm and duration are suitable parameters to generate tactile stimulation for encoding distance information. In this way the driver perceives countable vibro-tactile pulses, which indicate the distance in turn by turn instructions. The approach is found be simple way of encoding complex navigational information. © 2010 ACM. Source

2012 IEEE International Conference on Pervasive Computing and Communications Workshops, PERCOM Workshops 2012

Current autonomic vehicles in outdoor scenarios perform mobility operations with walking speed to ensure safety. For a faster mobility of autonomous vehicles, concrete knowledge of the environment is needed. This will be achieved through a dynamic context model based on sensor data with uncertainties from the environment. These uncertainties arise through existential uncertainty, consistency, and co/variance of and between the sensor data. To allow a flexible processing and to allow different approaches for object detection, object classification, and object tracking, data stream management technology is used. Therefore, a new algebra and operators based on the relational algebra are defined to preserve and process the uncertainties about the sensor data. © 2012 IEEE. Source

Langner M.,OFFIS | Peinke J.,Carl von Ossietzky University
European Physical Journal B

A procedure based on stochastic Langevin equations is presented and shows how a stochastic model of driver behavior can be estimated directly from given data. The Langevin analysis allows the separation of a given data-set into a stochastic diffusion- and a deterministic drift field. Form the drift field a potential can be derived. In particular the method is here applied on driving data from a simulator. We overcome typical problems like varying sampling rates, low noise levels, low data amounts, inefficient coordinate systems, and non-stationary situations. From the estimation of the drift- and diffusion vector-fields derived from the data, we show different ways how to set up Monte-Carlo simulations for the driver behavior. © 2015, EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg. Source

Heinzemann C.,University of Paderborn | Henkler S.,OFFIS
CompArch'11 - Proceedings of the 2011 Federated Events on Component-Based Software Engineering and Software Architecture - CBSE'11

Component based software engineering aims at re-using components in other systems. This requires a verification whether the component can safely interact with its communication partners in a new environment. Such verification is mandatory in case of safety-critical real-time systems where the communication is characterized by a varying number of components instances all being of the same type. Reuse can be facilitated by separating abstract communication protocol definitions and concrete component implementations. In contrast to standard refinement definitions for real-time systems, our definition explicitly takes varying numbers of communication partners into account. Additionally, we relax the strict conditions of a bisimulation to ease reuse of components. Along with our refinement definition, we provide a formal verification procedure to check for correct refinements which preserves properties verified for the abstract protocol definition. We evaluated our approach using a self-adaptive real-time system from the domain of autonomous train systems. The evaluation results show that checking for correct refinements is more efficient than re-verifying the desired properties on the refined component. © 2011 ACM. Source

Sinz F.H.,University of Tubingen | Lies J.-P.,University of Tubingen | Gerwinn S.,OFFIS | Bethge M.,University of Tubingen | Bethge M.,Max Planck Institute for Biological Cybernetics
Journal of Statistical Software

The statistical analysis and modeling of natural images is an important branch of statistics with applications in image signaling, image compression, computer vision, and human perception. Because the space of all possible images is too large to be sampled exhaustively, natural image models must inevitably make assumptions in order to stay tractable. Subsequent model comparison can then filter out those models that best capture the statistical regularities in natural images. Proper model comparison, however, often requires that the models and the preprocessing of the data match down to the implementation details. Here we present the Natter, a statistical software toolbox for natural images models, that can provide such consistency. The Natter includes powerful but tractable baseline model as well as standardized data preprocessing steps. It has an extensive test suite to ensure correctness of its algorithms, it interfaces to the modular toolkit for data processing toolbox MDP, and provides simple ways to log the results of numerical experiments. Most importantly, its modular structure can be extended by new models with minimal coding effort, thereby providing a platform for the development and comparison of probabilistic models for natural image data. © 2014 American Statistical Association. All rights reserved. Source

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