Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    Are you sure? Prediction revision in automated decision-making
    ( 2021)
    Burkart, Nadia
    ;
    Robert, Sebastian
    ;
    Huber, Marco
    With the rapid improvements in machine learning and deep learning, decisions made by automated decision support systems (DSS) will increase. Besides the accuracy of predictions, their explainability becomes more important. The algorithms can construct complex mathematical prediction models. This causes insecurity to the predictions. The insecurity rises the need for equipping the algorithms with explanations. To examine how users trust automated DSS, an experiment was conducted. Our research aim is to examine how participants supported by an DSS revise their initial prediction by four varying approaches (treatments) in a between-subject design study. The four treatments differ in the degree of explainability to understand the predictions of the system. First we used an interpretable regression model, second a Random Forest (considered to be a black box [BB]), third the BB with a local explanation and last the BB with a global explanation. We noticed that all participants improved their predictions after receiving an advice whether it was a complete BB or an BB with an explanation. The major finding was that interpretable models were not incorporated more in the decision process than BB models or BB models with explanations.
  • Publication
    Modular and scalable automation for field robots
    ( 2021)
    Osten, Julia
    ;
    Weyers, Catrin
    ;
    Bregler, Kevin
    ;
    Emter, Thomas
    ;
    Petereit, Janko
    This article describes a modular and scalable charging and navigation concept for electrified field robots and other agricultural machines. The concept consists of an underbody charging system on a trailer and a modular navigation box. The underlying conductive charging process is compared to other charging techniques. Charging time in relation to charging current and mean power consumption in field use is displayed. In the navigation box, data of various sensors are combined by means of multi-sensor fusion regarding the precise time of arrival. Time synchronization is achieved by a novel method for compensating the data latency jitter by employing Kalman based timestamp filtering. Furthermore, navigation functionalities, such as motion planning and mapping, are presented.
  • Publication
    Explanation Framework for Intrusion Detection
    ( 2021)
    Burkart, Nadia
    ;
    Franz, Maximilian
    ;
    Huber, Marco F.
    Machine learning and deep learning are widely used in various applications to assist or even replace human reasoning. For instance, a machine learning based intrusion detection system (IDS) monitors a network for malicious activity or specific policy violations. We propose that IDSs should attach a sufficiently understandable report to each alert to allow the operator to review them more efficiently. This work aims at complementing an IDS by means of a framework to create explanations. The explanations support the human operator in understanding alerts and reveal potential false positives. The focus lies on counterfactual instances and explanations based on locally faithful decision-boundaries.
  • Publication
    A Study on Trust in Black Box Models and Post-hoc Explanations
    ( 2019)
    El Bekri, Nadia
    ;
    Kling, J.
    ;
    Huber, M.
    Machine learning algorithms that construct complex prediction models are increasingly used for decision-making due to their high accuracy, e.g., to decide whether a bank customer should receive a loan or not. Due to the complexity, the models are perceived as black boxes. One approach is to augment the models with post-hoc explainability. In this work, we evaluate three different explanation approaches based on the users' initial trust, the users' trust in the provided explanation, and the established trust in the black box by a within-subject design study.
  • Publication
    Situation responsive networking of mobile robots for disaster management
    ( 2014)
    Kuntze, Helge-Björn
    ;
    Frey, Christian W.
    ;
    Emter, Thomas
    ;
    Petereit, Janko
    ;
    Tchouchenkov, Igor
    ;
    Müller, Thomas
    ;
    Tittel, Martin
    ;
    Worst, Rainer
    ;
    Pfeiffer, Kai
    ;
    Walter, Moriz
    ;
    Rademacher, Sven
    ;
    Müller, Fabian
    If a natural disaster like an earthquake or an accident in a chemical or nuclear plant hits a populated area, rescue teams have to get a quick overview of the situation in order to identify possible locations of victims, which need to be rescued, and dangerous locations, hich need to be secured. Rescue forces must operate quickly in order to save lives, and they often need to operate in dangerous enviroments. Hence, robot-supported systems are increasingly used to support and accelerate search operations. The objective of the SENEKA concept is the situation responsive networking of various robots and sensor systems used by first responders in order to make the search for victims and survivors more quick and efficient. SENEKA targets the integration of the robot-sensor network into the operation procedures of the rescue teams. The aim of this paper is to inform on the objectives and first research results of the ongoing joint research project SENEKA.
  • Publication
    SENEKA - sensor network with mobile robots for disaster management
    ( 2012)
    Kuntze, Helge-Björn
    ;
    Frey, Christian W.
    ;
    Tchouchenkov, Igor
    ;
    Staehle, Barbara
    ;
    Rome, Erich
    ;
    Pfeiffer, Kai
    ;
    Wenzel, Andreas
    ;
    Wöllenstein, Jürgen
    Developed societies have a high level of preparedness for natural or man-made disasters. But such incidents cannot be completely prevented, and when an incident like an earthquake or an accident in a chemical or nuclear plant hits a populated area, rescue teams need to be employed. In such situations it is a necessity for rescue teams to get a quick overview of the situation in order to identify possible locations of victims that need to be rescued and dangerous locations that need to be secured. Rescue forces must operate quickly in order to save lives, and they often need to operate in dangerous environments. Hence, robot-supported systems are increasingly used to support and accelerate search operations. The objective of the SENEKA concept is to network the various robots and sensor systems used by first responders in order to make the search for victims and survivors more quick and efficient. SENEKA targets the integration of the robot-sensor network into the operation procedures of the rescue teams. The aim of this paper is to inform on the goals and first research results of the ongoing joint research project SENEKA.