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Master Thesis - Complex vehicle scenarios through structural reliability methods and fitness functions

Background 

Verifying the safety of autonomous driving systems (ADS) is one of the main challenges when it comes to their deployment. It is widely acknowledged that verification purely based on test driving is not feasible. Therefore scenario-based testing in a virtual environment can be an important component of a verification strategy. In this context selecting test cases that are both reasonable and challenging for an ADS is of utmost importance. Fitness functions have been proposed to limit the space of possible scenarios to ones that are reasonable for a certain scenario class. Typically, these fitness functions are used within an optimization framework to find challenging test cases.

Project Description 

In the scope of this master thesis project, the student is required to define/parametrize a number of complex (multi-)vehicle scenario classes.  For each of these scenario classes fitness functions need to be implemented and a statistical model for the parameters of the scenario class needs to be derived. Based on this, structural reliability methods are to be applied to (1) find critical test cases from a scenario class (2) compute a probability of failure of the ADS conditional on a scenario class. 

Qualifications 

We are looking for a student with the following skills:

  • Creative mindset
  • A strong background in mathematics, in particular probability theory and statistics (structural reliability is a plus)
  • Solid programming skills (Matlab, Python)
  • A passion for data analysis and programming

Further information and contacts

For questions regarding the project, please Contact Kilian Zwirglmaier, kilian.zwirglmaier@zenuity.com.

 

Or, know someone who would be a perfect fit? Let them know!

Munich, Germany


Munich, Germany Directions recruitment@zenuity.com

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