bachelor thesis/IDP: Generating Machine Learning data with FreeRTOS and EDF schedulers for an automotive environment

Overview: Today, hundreds of small applications run in modern cars that ensure roadworthiness with the help of sensors and control logic. These applications have special requirements with regard to real-time capability and reliability. To ensure these two basic conditions for the software, complex mathematical calculations are performed to determine the feasibility of the tasks. In addition, the final product has to prove the scarcity of occurring errors in field tests. Since the trend goes towards a consolidation of the software modules on only a few, but powerful, devices, a failure of these or a mutual blocking of the software modules has far-reaching consequences. In order to counter these increasing problems, the safe and efficient execution of the individual tasks must be guaranteed. Due to the increasing size and complexity of the code base in vehicles, this can no longer be achieved by mathematical and platform-specific approaches alone. Therefore, it is investigated whether AI-supported approaches, such as machine learning, are suitable. Machine learning, in turn, requires a solid and reliable data set as a basis, which is the subject of this thesis.

goal of your work:

The goal of this work is to adapt the existing data generator software from the research project MaLSAMi to embedded boards with the operating system FreeRTOS. The COBRA framework is to be used for task generation and the acquired data is to be prepared in such a way that it can be integrated into existing scheduleability analyses for evaluation.

Individual work packages:


  1.     Finding suitable task parameters to cover a wide range of tasks
  2.     Creating the tasks with COBRA for FreeRTOS
  3.     Connection of the task generator software to FreeRTOS
  4.     Representation of the obtained raw data with the help of the visualization software Grafana
  5.     Preparation of the acquired data for processing in Machine Learning

Your profile:


  • Knowledge in C , C ++ and Python
  • Independent and scientifically sound work

Previous lecture knowledge:


  • Introduction to Computer Architecture (ERA)
  • Basics - Operating Systems and System Software (IN0009) /or/ Operating Systems and Hardware-related Programming for Games (IN0034)
  • [optional] Operating systems - L4 microkernels (IN0012, IN2106, IN4258)

I look forward to receiving your applications.


Bernhard Blieninger