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Current Research Projects


SocialCars (since 2014):The overall objective of the SocialCars Research Training Group RTG is to research new methods and applications of decentralized, cooperative traffic management, that are enabled by new technological trends and developments such as Car-to-X communication. SocialCars focuses on the interplay of centralized management in the sense of classical traffic control, and decentralized management in the sense of the local goals of individual traffic participants. In order to comprehensively study this interplay while considering both the requirements of traffic participants and the constraints of the urban environment, we propose six fields of research, in which we investigate novel and interdisciplinary research questions. In these fields of research, we study problems related to behavioural aspects of traffic participants, societal objectives, technical and algorithmic foundations of communication, interaction, and dynamic geo-information, as well as models and methods of cooperative, (de)centralized traffic management. We research solutions to these problems that will enable us to realistically describe dynamic cooperative traffic systems, and to evolve and optimize such systems in accordance with societal objectives. The subject-specific competences of the participating professors provide excellent coverage of the six fields of research, from traffic management over dynamic geo-information systems and communication technology to modeling distributed decision-making, coordination, and cooperation. The RTG will be embedded in the Niedersächsisches Forschungszentrum Fahrzeugtechnik (NFF), thus the main working space of the students will be in the new NFF Research Building at TU Braunschweig where they are exposed to an inspiring environment with a wide range of academic and industrial competences from renowned colleagues working in the areas of mobility, traffic, and automotive engineering. At the same time, the students will also have a workspace at their home institutes, which will serve to foster their embedding in their own discipline. This substantially expands the qualification opportunities in the strategic research focus Mobility and Traffic of the NTH. Also see for additional documentation of research.


DeSIM (since 2013):The aim of the DeSIM project is to design and develop a distributed and high-scaling multi-agent-simulation (MAS). The presentation of the problem is focused on a detailed (micro)simulation view of each agent, where the agent-based modelling and communication structure are analyzed too. When using detail views, there are different issues in performance scaling where decentralized simulation architecture can be useful. The research's objective is to compare different simulation environments e.g. agent-based, grid-based or high-performance-computing and to create or enhance a framework with design and scaling structures. In order to validate our results, we use the domain of cooperative traffic simulation, including use cases such as decentralized routing, grouping and platooning, and information dissemination. There are tools simplifying the requirements of abstract design, visualization, progression or rather improvement of the current systems. The project combines different points of perception of distinct fields of research.


CONNECT (since 2013):Product Data Management (PDM) is an essential subarea within Product Life Cycle Management (PLM) and therefore of significant importance for industry. The increasing global collaboration in the field of product development along with the rising global competition requires new concepts and technologies to support the processes of the Product Data Management appropriately. Besides the safe, efficient and scalable management of product data, the seamless integration of trustworthy collaboration partners is one of the main focuses. In CONNECT, MEClab supports Volkswagen in the development and testing of innovative concepts to realize and optimize processes and technologies for the implementation of a global Product Data Management Systems (K-PDM).

Self-Organizing Robot Teams for Disaster Management (Robotic FireFighters)

Self-Organizing Robot Teams for Disaster Management (since 2013):Recent disasters, such as the Fukushima catastrophe, the attack on the WTC, the Gulf of Mexico oil spill, or the Mont Blanc Tunnel fire, have shown that post disaster management tasks still require considerable human intervention, even though the humans entering affected areas risk their health and lives, causing human tragedy and immense cost for national economies. With view to keeping these dangers and costs at bay, and to increasing effectiveness of disaster management operations, our long-term vision is that teams of autonomous robots equipped with sensors, manipulators, and communication capabilities will be able to enter dangerous, polluted or contaminated areas and to manage all the necessary tasks (e.g. search for survivors, evacuate injured persons, remove safety critical materials, clean up) without the need for explicit human assistance. This inspires the notion of Robotic FireFighters (RFF), in analogy to human fire brigades, where firemen (and firewomen) work together towards the common goal of getting a disaster under control. Our partners in this project are:


eCult (since 2011):eCult (eCompetence and Utilities for Learners and Teachers) is a collaborative project in Lower Saxony, funded by the Federal Ministry of Education and Research. Eleven universities and two other institutions are taking part in this project. The goal is to improve the quality of teaching and learning at universities. Therefor the project participants exchange local experiences to enhance existing eLearning approaches and to develop new eLearning approaches together. The project focuses on eAssessment, teaching and learning organisation, and video-based teaching and learning. The MEC research group is active in the area of eAssessment. In this area we focus on decentralised systems to support programming and modeling exercises at universities.

IT Ecosystems

IT Ecosystems (since 2009):Classical approaches of computer science do not scale well for today's large and complex software-intensive systems. Software systems cannot be considered in isolation, since they are connected among each other and interact massively. Instead they are to be designed as parts of a larger IT Ecosystem. In analogy to biological ecosystems, IT Ecosystems are based on the balance between individuals (autonomy) and sets of rules (control) defining equilibria within an IT Ecosystem. Maintaining and continuously evolving IT Ecosystems requires deep understanding of this balance. The new research topic IT Ecosystems cuts across several research areas, including: emergence of system functions, extending classical engineering approaches, adaptive infrastructures, control of semantic diversity, and enhanced human-environment-machine interaction. These core areas are addressed by the newly established NTH focused Research School for IT Ecosystems, a cooperation of the Universities of Braunschweig, Clausthal, and Hannover. A joint demonstrator will present innovative research results in the context of a smart city application.

The goal of AIM is to investigate methods for decentralized, bottom-up organisation of complex software systems, with special focus on the emergence and adaptation of interaction mechanisms among automated actors in dynamic environments. AIM is a subproject of the IT ecosystems project funded by the Federal government of Lower Saxony.

Agent-oriented Distributed Data Mining using Computational Statistics (ADMIT)

Agent-oriented Distributed Data Mining using Computational Statistics (since 2011):Today's systems for managing critical infrastructure such as traffic, energy, or industry automation systems are highly complex, distributed, and increasingly decentralized. Multi-agent systems (MAS) provide an intuitive metaphor and configurable, robust and scalable methods for problem-solving and control in distributed, decentrally organized system. The purpose of Distributed Data Mining (DDM) is to provide algorithmic solutions for data analysis in a distributed manner to detect hidden patterns in data and extract knowledge necessary for decentralized decision making. A new promising area of research studies possibilities for coupling MAS and DDM by exploiting DDM methods for improving agents’ intelligence and MAS systems performance. In the ADMIT project we focus on methods for distributed estimation of parameters for the individual agents, agent groups, and system-level information models. Our approach is based on Computational statistics (CST), which includes a set of methods for approximate solution of statistical problems without complex statistical procedures. The goal of the ADMIT project is to develop an agent-oriented DDM framework, which includes a set of computationally effective, robust and easy to apply methods for models parameter estimation and allows easy incorporation into MAS applications to analyze models at different levels of MAS. The scientific research objectives of ADMIT are:

  1. To develop a conceptual architecture of agent-oriented DDM framework as well as a methodology of its usage in multiagent programming frameworks;
  2. To develop a set of computationally effective and reliable to bad data quality CST-based DDM methods, for efficient estimation of model parameters on the basis of distributed data as well as estimate the methods performance;
  3. To assess the impact of incorporation of the DDM framework to MAS-based applications (with the main focus on traffic and logistics domains).


Mobile2Learn (since 2010):The aim of this project, which is conducted within the nifbe (Lower Saxony Institute for Early Childhood Education and Development) framework, is to support parents of young children. Often, these parents do not have the knowledge about the activities and games that may be beneficial for their children - but at the same time, they do often not have enough spare time to inform themselves and do not know where they could do this if they wanted to. In the project, which is conducted by the group of Prof. Pinkwart together with the research group of Prof. Jörg Müller at TUC and the KVHS Goslar, we will design, implement and test a combination of a web-based community platform with mobile phone access and field activities. The hypothesis is that these two forms of interaction will have synergy effects in that increased interaction and communication on the web may lead to increased participation in presence activities, and vice versa.

MONtan-Leistungscluster Auto-Id (MONLaid)

MONtan-Leistungscluster Auto-IdDer Montan Leistungscluster AutoID ist eine fakultätsübergreifende Forschungsgemeindschaft mehrerer Institute der TU Clausthal. Er hat es sich zur Aufgabe gemacht, den Einsatz modernster Informationstechnologie im Bereich der Montan-Industrie (Maschinen- und Anlagenbau, Stahlerzeugung und Stahlverarbeitung, Bergbau und Hüttenwesen) voran zu treiben.

Dazu gehören unter anderem:

  • Funkbasierte Identifikationsverfahren, z. B. RFID (Radio Frequency Identification)
  • Optische Identifikation von Informationsträgern oder Objekte (Laser-Scanner)
  • Positionsbestimmungs- und Lokalisierungstechniken, z. B. LPR (Local Positioning Radar)

Erfassung und Optimierung von Geschäftsprozessen mit Hilfe von AutoID Die eingesetzten Technologien erzeugen hochqualitative Bestands- und Zustandsdaten der laufenden Prozesse, wodurch sie transparent in der Informationsverarbeitung darstellbar sind. Sie ermöglichen eine echtzeitfähige Koordination des Work in Progress (WIP). Die nahtlose vertikale Integration von den Fertigungsprozessen bis in die operative und strategische Unternehmensführung wird durch Informationstechnik erreicht, die in der Lage ist alle relevanten Prozessdaten (Ort, Zeit, Identität, Zustand) in Echtzeit zu erfassen und bearbeiten.

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Multiagent planning and decision-making in traffic networks (PLANETS)

Multiagent planning and decision-making in traffic networks (since 2009):PLANETS is a research project funded by the university "Niedersächsisch-Technische Hochschule" (NTH). The goal is to develop and evaluate innovative approaches for dynamic traffic management based on state-of-the-art technologies in information and communication technology. Main focus of the project is the integration of traffic and communication simulation, considering approaches from business information systems. The complexity of the broad field of technology is managed by interdisciplinary cooperation of expertise in the areas of traffic, business information systems and communication technology. Next to the Decision Support Group, the Institute of Computer Science at the Technische Universität Clausthal, the Institute for Traffic and Urban Engineering at the Technische Universität Braunschweig and the Institute for Communication Technology at the University of Hanover are involved in the project.

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