• Contact:

    M. Sc. Moritz Zink

  • Project group:

    Prof. Sax

  • Funding:

    Bundesministerium für Bildung und Forschung über das Deutsche Zentrum für Luft- und Raumfahrt e.V. (DLR)

  • Partner:

    TTTech Auto, Bosch, Frauenhofer, Asvin, Inchron, SGS, Deutsches Zentrum für Luft- und Raumfahrt, Osseno, Merantix, Validas, TÜV, Hochschule Hamm-Lippstadt, SafeTrans, Technische Hochschule Ingolstadt, Universität Stuttgart, Universität Oldenburg, Humboldt Universität Berlin

  • Startdate:


  • Enddate:



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Digitalization in vehicles represents an important innovation driver with high economic significance for Germany as a business location. On the one hand, the implementation of highly automated and autonomous driving functions results in high requirements for the verification of safety properties of these systems. 

Project goals

The AutoDevSafeOps project will provide an important building block for modular updates of safety-critical driving functions - a key technology that transcends previous system boundaries and thus has high strategic importance for the competitiveness and innovation dynamics of Germany as a high-tech location.

Other Project goals

  • Integrated development and operation of safe automotive systems
  • Provide tools, models and processes for the rapid and continuous delivery of safety-critical vehicle software, also with regard to machine learning and certifiability 

KIT Participation

In the project, KIT is developing methods for quantifying and evaluating the completeness of system specifications with a focus on AI functions (AP4). 

In addition, new approaches for the automatic synthesis of safety specifications, e.g., contracts, based on monitoring data are being developed and transferred to existing automotive systems (AP3).

For the integration of the researched methods into the development process, KIT participates in the definition and specification of the update process (AP2). The focus is on the orchestration of data usage between the Dev and Ops phases with the goal of optimizing the selection of training data for AI functions and the cross-fleet evaluation of update confidence and robustness.

For the topic area of scenario-based testing, KIT is developing methods to identify the relevant ODD spaces and scenarios for a safety-critical function update of a CPSoS (AP4).

In addition, KIT, in cooperation with the project partners, will develop a delta-based and parameterizable modeling technique for CPS and CPSoS as a basis for incremental modeling and validation (AP3).

ITIV Participation

The Institute for Information Processing Technology (ITIV) will contribute in the following areas:

  • Quantification and evaluation of the completeness of system specifications.
  • Process for life cycle management of updates of cooperative automotive systems with AI components
  • Method for automatic synthesis of safety specifications of existing automotive systems