CoFFF

The project develops methods and open source tool support for the synthesis of fail-operational architectures, in order to contribute to a more efficient design of autonomous driving functions.

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

Your contact

Simon Barner

+49 89 3603522 22
barner@fortiss.org

More information

AutoFOCUS3 Model-based development of embedded systems
Field of competence Model-based Systems Engineering Efficiently develop, validate and maintain reliable systems
Demonstrator fortissimo Rover Model-based system engineering: from sensor technology to safe autonomous driving functions

Project partner

Publications

2023 A Toolchain for Synthesizing and Validating Safety Architectures Yuri Gil Dantas, Tiziano Munaro, Carmen Cârlan, Vivek Nigam, Simon Barner, Shiqing Fan, Alexander Pretschner, Ulrich Schöpp and Sergey Tverdyshev SN Computer Science, 4(4):335, 2023. Details DOI BIB
2022 A Model-based System Engineering Plugin for Safety Architecture Pattern Synthesis Yuri Gil Dantas, Tiziano Munaro, Carmen Cârlan, Vivek Nigam, Simon Barner, Shiqing Fan, Alexander Pretschner, Ulrich Schöpp and Sergey Tverdyshev In Proceeding of the 10th International Conference on Model-Driven Engineering and Software Development (MODELSWARD), pages 36–47, SCITEPRESS. Details DOI BIB

Name	Purpose	Lifetime	Type	Provider
_pk_id	Used to store a few details about the user such as the unique visitor ID.	13 months	HTML	Matomo
_pk_ref	Used to store the attribution information, the referrer initially used to visit the website.	6 months	HTML	Matomo
_pk_ses	Short lived cookie used to temporarily store data for the visit.	30 minutes	HTML	Matomo
_pk_cvar	Short lived cookie used to temporarily store data for the visit.	30 minutes	HTML	Matomo
_pk_hsr	Short lived cookie used to temporarily store data for the visit.	30 minutes	HTML	Matomo