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EDI coordinates the BMVI-funded project RELAI
The project RELAI - Risk Estimation with a Learning AI is funded by the Federal Ministry of Transport and Digital Infrastructure (BMVI). With our cooperation partners IPG Automotive GmbH, Fraunhofer IOSB and the University of Stuttgart, the EDI hive Framework is used to generate a catalog of synthetic test scenarios for the development of autonomous vehicles and to make them available to the public via a web portal. Here, AI algorithms evaluate real, challenging traffic situations in mixed traffic and convert them into synthetic scenarios. The main focus is on the behavior of pedestrians and cyclists in critical situations as well as the expectations of these road users on the behavior of autonomous vehicles. Another important goal is to be able to automatically calibrate different simulation environments (including a virtual reality (VR) environment) through the synthetic test scenarios. It also indicates in which areas or sections specific test scenarios can be carried out in real road tests. The automatically generated test scenarios are made accessible to the public via a web portal on the EDI hive platform. In addition, the EDI hive framework is directly linked to the mCloud, the data portal of the BMVI, so that this project also helps to build up a comprehensive mobility database in Germany.
Projects & Activities
EDI was selected with "EDI Hive an Intelligence Platform to Automate Tooling Machines" as one of 25 European Startups by Allianz Industrie 4.0 Baden-Württemberg. The event supports the aim of EDI: "Long-term Innovation Cooperation" with SMEs.
Deeptech4Good#Stuttgart is the second business event of the DeepTech4Good Acceleration Programme and took place on 7th November 2018 in Haus der Wirtschaft, Stuttgart. Co-CEO Dr. Thomas Freudenmann and Creative Director Hitomi Fukatani presented EDI's technology at DeepTech4Goods Stuttgart on 7th November 2018. EDI was chosen as one of the finalists to present at this event (https://www.deeptechforgood.eu/ )
Dynamic Risk Management: With the support of PTV, EDI and KAIT (Kanagawa Institute of Technology) work on developing and validating algorithms for autonomous vehicles that are accepted by humans and increase safety in traffic.
As member of the de:hub for Artificial Intelligence (AI) Karlsruhe EDI presented how applied AI technology works.
LionAID aims to develop an intelligent diagnosis system for lithium ion batteries of electric vehicles (Partners: Fraunhofer IEE Kassel, FKFS, CTC cartech company GmbH and EDI GmbH)
CTC cartech company's smart battery operation system LionTelligence powered by EDI hive
OTEC introduced the OTEC Finishing Expert powered by EDI hive on the AMB in Stuttgart.
OTEC and EDI cooperate in the field of Industry 4.0 for developing new services, such as intelligent machine automation.
EDI develops a workflow-based application on the EDI hive platform for supporting the Mercedes-Benz product strategy.
Iodata and EDI cooperate in the areas of data analytics and visualization as well as prediction model generation for offering innovative business intelligence solutions (e.g. predictive workload planning)
EDI offers and manages tests for suppliers and OEMs on KIT test facilities. In this case IVH, EDI and KIT (Institute of Vehicle System Technology) work together to assess the durability of tires under different driving conditions.
EDI's Co-CEO Dr. Thomas Freudenmann consults Chinese companies on the subject of intelligent productionusing artificial intelligence (Made in China 2025).
EDI's Co-CEO Dr. Thomas Freudenmann leads a round table discussion on the topic of safety and driver acceptance of autonomous driving algorithms in Berlin, Germany.
EDI and KIT develop a task planning software for testing and validation, the Semantic Validation Process (SVP), for the chemical industry using a patented semantic algorithm.
EDI and KIT work together on optimising the combustion process in power plants for reducing NOx emissions.
EDI hive Framework
Business as usual - on the next level!
Automated semantic tagging of your data and files
Customised AI-applications based on your expert knowledge and business logic
Automatic suggestion of relevant information without manual search
Transparent AI-based prediction models allow engineers to understand complex processes and to automate machines
Easy integration into existing or new systems / tools (e.g. PLM-Systems)
Application to formalize expert and process knowledge (based on KIT patent)