ROUTINE
Living lab for the transfer of digital health applications and AI into healthcare
Start: 01/2021
End: 12/2023
To prove the safe function of highly automated vehicles, firmly defined scenario catalogs are used for maneuver-related evidence according to the current state of the art, as well as real-time data comprising several million driving kilometers for statistical verification.
To develop new vehicles with a 4/5 automation level, achieving a selective acquisition of relevant and critical driving situations, significant environmental data as well as raw data of the vehicle sensor system while driving are indispensable. This data is needed to validate, improve and reproduce the decisions made by artificial intelligence (AI), with the aim of thus achieving the necessary test coverage for future functionalities.
Within the KIsSME project, AI-based algorithms will be applied to enable on-board systems to recognize relevant and critical scenarios in real time and to selectively acquire raw data and scenario descriptions for this purpose. The AI-based algorithms will enable an inherent learning capability that continuously improves the recognition of critical situations and the associated relevant data to increase the information density of the data used for testing when developing level 4/5 automated systems, while simultaneously significantly reducing the associated data volume required as well as the effort needed to ensure data protection.
In this research focus, the FZI prioritizes the topics of Artificial Intelligence (AI) as well as human and AI engineering. In addition, the FZI deals with questions on dedicated AI hardware and predictive AI.
Intelligent solutions for the transportation of people and goods represent a focus topic of FZI’s application research. Particular attention is paid to public transport, the application of artificial intelligence, the further development of driving functions and their safeguarding, and open source & open data.
Funding notice:
The KisSME project is funded by the Federal Ministry for Economic Affairs and Climate Action (BMWK).
Project partners:
Living lab for the transfer of digital health applications and AI into healthcare
Next-generation distributed, continuously learning onboard power management system
Safe use of automated shuttle vehicles in urban traffic through supporting infrastructure networking
Broker for dynamic production networks
Artificial Intelligence for Work and Learning in the Karlsruhe Region
Security at multiple system layers based on chains of trust and isolation
Software Engineering of Industrial, Hybrid Quantum Applications and Algorithms
Competence Cluster Anonymization for Interconnected Mobility Systems
Climate-friendly, neuromorphic and for a sustainable transport infrastructure of the future.
Actively addressing the challenges posed by climate change and using them as an opportunity for the German economy.