Friday, July 08, 2022

Systematic Identification and Extraction of Edge Cases from big driving datasets Topic

Topic and Goal of the Thesis

The development of automated driving has progressed towards a point where the public testing is conceivable. To some extent it is already underway. The most situations, which occur on roads, have already been classified. However, there are still situations which are not yet logged or happen on rare occasions. These are often referred to as edge cases. In this thesis, a conclusive concept for the handling and extraction of edge cases is to be defined based on a given definition of the term.

Working Points

  • Literature research on definitions, meaning and handling of edge cases in various fields
  • Development of a (modular) concept for the systematic identification and extraction of edge cases from driving data
  • Implementation of the concept and exemplary execution using given datasets captured in real traffic
  • Documentation of the results
  • Co-authorship of a scientific paper on the topic


  • Programming experience with Python (and C++ if possible)
  • Experience in the field of big data would be good
  • Good English or German language skills
  • Reliability and enjoyment of working independently

Note: Please attach brief resume and grade summary.


Lennart Vater M.Sc.
+49 241 80 23891

Type of work



Earliest possible date

Prior knowledge

Python and C++


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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