Publicly Funded Projects
Collaboration with partners from business and science in publicly funded projects enables us to identify technological trends faster and be actively involved in shaping new business fields right from the start.
Our active participation in research cooperations also makes us a key member of international research and development networks. It also offers us the possibility to align ourselves well for handling future challenges. On account of the wide spectrum of topics covered by the company, BCAI is a partner who can offer a great deal of experience in numerous areas and in many cases even ensure early market access. In order to define future research fields, we are also represented as experts in various bodies at both national and international level. Here we present a selection of top publicly funded projects BCAI is involved in.
More information below
OntoCommon
Ontology-driven data documentation for industry commons
OntoCommons lays the foundation for interoperable, harmonised and standardised data documentation through ontologies, facilitating data sharing and pushing data-driven innovation, to bring out a truly Digital Single Market and new business models for European industry, exploit the opportunities of digitalisation and address sustainability challenges. This will be achieved by developing the Ontology Commons EcoSystem (OCES) - a set of ontologies and tools that follows specific standardisation rules - and provide a sustainable approach, making the data FAIR (Findable, Accessible, Interoperable and Reusable). Moreover, the OCES implements practical and user-friendly mechanisms of intra- and cross-domain interoperability focusing.
More Details on: EU-Portal, Project-Website, LinkedIn
Project Details
- BCAI Topics: AI Methods for Semantic Digital Twins, Ontologies, and Knowledge Graphs
- Project duration: 01/11/2020 to 31/10/2023
- Funding program: This project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 958371
- Project partners:
Contact at BCAI
DOME 4.0
Digital Open Marketplace Ecosystem (DOME) 4.0
DOME 4.0 aims at an intelligent semantic industrial data ecosystem for knowledge creation across the entire materials to manufacturing value chains. The ecosystem provides a sustainable solution to the information silos problem related to the past efforts and puts forward a formal, ontology-based documentation for open and confidential data spaces applicable to future and current projects thereby delivering added value.
More Details on: EU-Portal, Project-Website, LinkedIn
Project Details
- BCAI Topics: AI Methods for Semantic Digital Twins, Ontologies, and Knowledge Graphs
- Project duration: 01/12/2020 to 30/11/2024
- Funding program: This project has received funding from the European Union’s Horizon 2020 research and Innovation Programme under Grant Agreement No. 953163.
- Project partners:
- Cambridge Nanomaterials Technology Ltd. (CNT)
- Fraunhofer Gesellschaft zur Förderung der angewandten Forschung e.V. (Fraunhofer)
Contact at BCAI
DataCloud
The overall vision of the DataCloud project is the creation of a novel paradigm for Big Data pipeline processing over heterogeneous resources encompassing the Computing Continuum, covering the complete lifecycle of managing Big Data pipelines.
More Details on: EU-Portal, Project-Website, Twitter
Project Details
- BCAI Topics: AI Methods for Semantic Digital Twins, Ontologies, and Knowledge Graphs, Scalable Semantic Data Analytics
- Project duration: 01/01/2021 to 31/12/2023
- Funding program: This project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 101016835
- Project partners:
Contact at BCAI
KITOS
The overall goal is to provide industrial networks with the necessary dynamics and methods for self-healing and self-optimization in order to generate the necessary agility and reliability that future production processes in Industry 4.0 require. TSN is used as the basis for networking. Kl algorithms are developed which support configuration tools in decision making in order to achieve a more efficient use of resources as well as higher performance configurations. For active operation, the network management is hardened against failures by means of Kl supported error detection and adaptation mechanisms.
More Details on: BMBF-Website
Project Details
- BCAI Topics: Automated design of optimization algorithms, Sensors & Networks
- Project duration: 04/01/2020 - 05/31/2023
- Funding program: This project has received funding from the Federal Ministry of Education and Research (BMBF)
- Project partners:
Contact at BCAI
DARPA-GARD
Despite their incredible success in many computer vision tasks, deep neural networks are known to be sensitive to adversarial attacks; small perturbations to an input image can lead to large changes in the output. The goal of this project is to find defense methods that provides guaranteed performance against adversarial attack.
Find more details on the Project-Website
Project Details
- BCAI Topics: robust and safe AI
- Project duration: 5 years
- Funding program: This project has received funding from DARPA (US government agency)
- Project partners: Carnegie Melon University
Contact at BCAI
KI-Embedded
The goal of KI-Embedded is to develop, model and control drive systems based on AI technology. One focus here is the efficient implementation of the developed AI models and processes on embedded systems for onboard applications of future vehicles. The methods are to be developed exemplarily using two application examples (smart monitoring with AI-based virtual sensors and model-based control of the fuel cell stack) and both the short-term and the long-term potential of the research project are to be demonstrated.
Project Details
- BCAI Topics: AI methods for data acquisition, modeling, and validation with focus on application on embedded systems
- Project duration: 01.09.2021 – 30.06.2024
- Funding program: „Neue Fahrzeug- und Systemtechnologien“ - funding program BMWi (Federal Ministry of Economics and Technology)
- Project partners:
Contact at BCAI
KI Absicherung
The goal of the KI Absicherung is to make the safety of AI-based function modules for highly automated driving verifiable.The vision of the project is to build an industry consensus for a general safeguarding strategy for AI functions.
Project Details
- BCAI Topics: Active semi-supervised learning, Plausibility check via Saliency Maps, Self-supervised pretraining for performance and robustness improvement, technology analysis support
- Project duration: 36 Monate: 01.07.2019 – 30.06.2022
- Funding program: This project has received funding from the Federal Ministry for Economic Affairs and Energy
- Project partners:Volkswagen AG, Audi AG, BMW Group, Opel Automobile GmbH, Continental AG, EFS GmbH, Valeo, ZF AG, ASTech GmbH, Intel Corporation, luxoft, Mackevision GmbH, Merantix AG, QualityMinds GmbH, Umlaut SE, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, Deutsched Zentrum für Luft- und Raumfahrt e.V., Fraunhofer IAIS, Fraunhofer IKS, Forschungszentrum Informatik, Bergische Universität Wuppertal, Technische Universität München, Universität Heidelberg, BIT Technology Solutions GmbH, neurocat GmbH, understand.ai, European Center for Information and Communication Technologies (EICT)
Contact at BCAI
ULPEC
ULPEC was a research project funded under the action Smart System Integration (SSI) of the European Commission Horizon 2020 framework program.
The project has been running between January 2017 and June 2021, and was led by the University of Bordeaux. ULPEC was formed by a consortium of nine European universities, startups, and industrial research labs, working in the fields of microelectronics, materials, modelling, and applications. The long term goal of ULPEC is to develop advanced vision applications with ultra-low power requirements and ultra-low latency. The output of the ULPEC project was a demonstrator connecting a neuromorphic event-based camera to a high speed ultra-low power consumption asynchronous visual data processing system (Spiking Neural Network with memristive synapses).
The part of Bosch was to contribute neural network algorithms that allow the use of event-based vision and spiking neural networks in the domain of driver assistance and automated driving. Furthermore Bosch worked on requirements for the use of novel technologies for driving assistants, and an exploitation roadmap for neuromorphic and memristive technologies within the scope of ULPEC. Bosch teams from BCAI Research and Corporate Research in Renningen and Hildesheim were part of the project.
More Details on: Project-Website
Project Details
- BCAI Topics: Neural network algorithms that allow the use of event-based vision and spiking neural networks in the domain of driver assistance and automated driving
- Project duration: 01/2017 to 06/2021
- Funding program: This project has received funding action Smart System Integration (SSI) of the European Commission Horizon 2020 framework program
- Project partners: University of Bordeaux, CNRS, IBM Research, University of Twente, Sorbonne University, ETH Zurich, TSST, Prophesee, and Bosch
Contact at BCAI
TAILOR
Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization
The purpose of TAILOR is to build a strong academic-public-industrial research network with the capacity of providing the scientific basis for trustworthy AI leveraging and combining learning, optimization and reasoning for realizing AI systems that incorporate the safeguards that make them reliable, safe, transparent and respectful of human agency and expectations.
The network will be based on a number of innovative state-of-the-art mechanisms. A multi-stakeholder strategic research and innovation research roadmap coordinates and guides the research in five basic research programs. Each program forming virtual research environments with many of the best AI researchers in Europe addressing the major scientific challenges identified in the roadmap. Another pillar of the network is a collection of mechanisms supporting innovation, commercialization and knowledge transfer to industry. To support network collaboration, TAILOR provides mechanisms such as AI-Powered Collaboration Tools, a PhD program, and training programs, and a connectivity fund to support active dissemination across Europe.
More Details on: Project-Website
Project Details
- BCAI Topics: We participate in the Smart Industry sector of the Industry & Innovation work package. We contribute with our expertise to the Theme Development Workshops to identify strategic research areas and provide industrial use cases for potential hackathons or longterm benchmarks for the academic community
- Project duration: 01/09/2020 - 31/8/2023
- Funding program: This project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 952215
- Project partners:
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- Linköping University
- Consiglio Nazionale delle Ricerche
- Institut national de recherche en informatique et en automatique
- University College Cork
- KU Leuven
- La Sapienza
- Leiden University
- University of Lisbon
- AI and Machine Learning Group, Universitat Pompeu Fabra
- Alma Mater Studiorum – Universita di Bologna (UNIBO)
- Bar-Ilan University
- Eindhoven University of Technology
- Centre National de la Recherche Scientifique
- Jožef Stefan Institute
- TU Darmstadt
- University of Bristol
- University of Freiburg
- University of Oxford
- University of Trento
- Vrije Universiteit Brussel
- Charles University
- Commissariat à l’Energie Atomique et aux Energies Alternatives
- Université d’Artois
- Czech Institute of Informatics, Robotics and Cybernetics
- Delft University of Technology
- German Research Center for Artificial Intelligence
- Ecole Polytechnique Fédérale de Lausanne
- Fondazione Bruno Kessler
- Fraunhofer IAIS
- Graz University of Technology
- Institut d’Investigació en Intel·ligència Artificial – Consejo Superior de Investigaciones Científicas
- Lancaster University
- National and Kapodistrian University of Athens
- NEO: Networking and Emerging Optimisation
- Poznan University of Technology
- RWTH Aachen Center for Artificial Intelligence
- Siena Artificial Intelligence Lab, University of Siena
- Slovak research center for artificial intelligence
- Netherlands Organisation for applied scientific research
- Università di Pisa
- Université Grenoble Alpes
- University of Basel, AI Research Group
- Valencian Research Institute for Artificial Intelligence
- Volkswagen Group Machine Research Lab
- Engineering – Ingegneria Informatica SPA
- Tieto Finland Oy
- Philips Electronic Nederland B.V.
- Electricite de France
- Imperial College of Science Technology and Medicine
- ZF Friedrichshafen AG
- Luxembourg Institute of Health
- Centraal Bureau voor de Statistiek
- Bosch GmbH
- ABB AB
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Contact at BCAI