Wednesday, May 31, 2023

Machine learning-based Generation of realistic Edge-Case traffic scenarios

Topic and Goal of the Thesis

How can we test, if automated driving is safe?

This is a central question in research in safety assurance for automated vehicles. A central approach for this is scenario-based testing. Particularly exciting are edge cases that challenge the system but are still intended to be realistic. However, such edge cases rarely occur in reality and can hardly be found in data.

In this thesis, a method for generating realistic edge cases of driving scenarios based on real data shall be developed. In particular, it shall be analyzed how the behaviour of dynamic road users can be without creating driving scenarios that never occur in traffic. Both, machine learning and rule-based approaches can be used for this purpose.

Working Points

  • Literature research on the topics of data classification and Edge-Cases in the context of driving scenarios
  • Development of a methodology to classify and generate realistic Edge-Cases for dynamic road users
  • Testing of generated scenarios in simulation
  • Validation of the methodology based on real intersection data


  • Experience with python
  • Good English or German language skills
  • Reliability, commitment and enjoyment of working independently as well as methodically
  • Enthusiasm for automated driving

Note: Please attach brief resume and grade summary.


Christoph Glasmacher M. Sc.
+49 241 80 25611

Type of work



Earliest possible date

Prior knowledge



Deutsch, Englisch

Research area

Fahrzeugintelligenz & Automatisiertes Fahren


Institute for Automotive Engineering
RWTH Aachen University
Steinbachstraße 7
52074 Aachen · Germany
+49 241 80 25600

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