Master's thesis in the field of autonomous driving from October 2024

Shape the future of mobility around highly automated driving with us!

The acquisition and evaluation of real driving data is essential for the design and validation of highly automated driving systems. As part of your Master's thesis, you will be involved in the development of a method for transferring data collected by fleet vehicles with series technology into a knowledge-based system in order to be able to make a statement about the probability of occurrence of specific driving scenarios in the population of real traffic. The method includes the qualitative abstraction and parameterization of the driving events recorded by the sensors as well as the estimation of the distributions and correlations of scenario parameters.

In your master's thesis, you will develop and evaluate various approaches for the parameterization of driving scenarios. Among other things, the parameterization should enable a sufficiently accurate reconstruction of the real trajectories, be suitable for statistical modeling and provide suitable parameters for the variation in Software in the Loop (SiL) testing. General metrics for evaluating the suitability of specific parameter representations for these use cases have already been developed and implemented.

Specifically, you will deal with the following tasks:

• Familiarization with the method of the higher-level project
• Acquiring an understanding of the use cases, their requirements for parameterization and the metrics for evaluating the fulfillment of requirements
• Theoretical consideration of different approaches to parameterization
• Implementation of one or more methods for parameterization
• Validation or evaluation of the implemented method(s) based on the previously established requirements

Further information on the method of the superordinate project can be found in the following publications:

• https://doi.org/10.48550/arXiv.2404.06288
• https://doi.org/10.48550/arXiv.2203.03515

• Master's students in natural science subjects such as mathematics, computer science, physics or comparable subjects
• Good programming skills in Python
• Good knowledge of mathematics
• Ideally knowledge of cluster computing and analyzing large amounts of data with Apache Spark
• Written and spoken German and English
• Independent and structured way of working

Additional information:

Of course, we cannot do without formalities. Please apply online only and attach a CV, current certificate of enrollment stating the semester of study, current transcript of records, relevant certificates (max. total size of attachments 5 MB) and mark your application documents as "relevant for this application" in the online form.

Further information on the recruitment criteria can be found"here".

Nationals from countries outside the European Economic Area should send their residence/work permit with their application.

We particularly welcome online applications from severely disabled persons and persons with equivalent disabilities. If you have any questions, you can also contact the site's representative for severely disabled employees at sbv-sindelfingen@mercedes-benz.com, who will be happy to support you in the further application process after your application.

Please understand that we no longer accept paper applications and that there is no entitlement to return postage.

If you have any questions about the application process, please contact HR Services by e-mail at myhrservice@mercedes-benz.com or by phone: 0711/17-99000 (Monday to Friday between 10 a.m. - 12 p.m. and 1 p.m. - 3 p.m.).