just better DATA
Efficient and highly accurate data generation for AI applications in autonomous driving
Start: 07/2022
End: 09/2025
SASVI addresses the design of trustworthy IT systems by considering the following issues:
These issues are solved in SASVI by trust chains with end-to-end isolation, which consist of secure RISC-V-based processor architectures, hardware-based operating system components, hardware-based root-of-trust components, and trusted runtime environments (TEE).
The goal of the FZI in SASVI is to develop a cross-system layer concept for trust chains with end-to-end isolation. This includes the layers of hardware/software components, operating system, and IIoT applications based on secure processor architectures, hardware-based operating system components, hardware-based root-of-trust components, and TEE. In addition, special focus is placed on the secure and consistent integration of the components into a trustworthy overall system that is suitable for industrial applications. In order to achieve the widest possible impact of the security technologies developed, SASVI relies on the open RISC-V architecture for hardware components. The resulting end-to-end trustworthy overall system will then be researched and evaluated using use cases in critical or sensitive infrastructures. These cases can be found, for example, in water supply and wastewater disposal. The technical goals, listed from hardware to the system layer, are the development of:
In addition, the FZI aims to research novel isolation mechanisms that can be implemented with open-source hardware and software components in the embedded sector. In this way, methods and concepts for future-proof IIoT systems are to be developed at various levels that can be applied in further research projects for Industry 4.0 and the automotive sector.
In this research focus, the FZI investigates and conveys innovative concepts, methods for protecting IT systems, and legal framework conditions to enable secure digitalization.
Further links:
Project partners:
Efficient and highly accurate data generation for AI applications in autonomous driving
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