Efficient model-based system architectures for fail-safe autonomous driving functions
CoFFF
Project description
Autonomous driving functions must continue to function correctly, at least temporarily and with degraded performance, even in the event of hardware or software faults. The complexity of autonomous driving functions and today's automotive hardware/software stacks makes the design of efficient fail-operational architectures a challenging and expensive task. Therefore, the project investigates methods for the automatic synthesis of architectures and platform configurations for fail-operational functions.
CoFFF builds on fortiss‘ AutoFOCUS3 model-based engineering tool and research platform as well as the autonomous driving use case investigated in the fortissimo demonstrator.
Research contribution
The project investigates innovative architecture synthesis methods based on model-based engineering and formal methods. It provides an extension for fortiss‘ open source model-based engineering tool AutoFOCUS3 that will be used to derive a fail-operational architecture for the fortissimo autonomous driving demonstrator simulation.
Funding
Supported by Huawei Technologies Düsseldorf GmbH
Project duration
01.01.2021 - 31.01.2022
Software
Model-based development of embedded systems
fortiss Lab
Developing safe autonomous systems efficiently and automating testing
Contact
Project partner
Publications
- A Toolchain for Synthesizing and Validating Safety Architectures SN Computer Science, 4(4):335, 2023. Details DOI BIB
- A Model-based System Engineering Plugin for Safety Architecture Pattern Synthesis In Proceeding of the 10th International Conference on Model-Driven Engineering and Software Development (MODELSWARD), pages 36–47, 2022. SCITEPRESS. Details DOI BIB