Student for thesis: Methodology, scenario categorization, fleet data

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Number: MER0003XBU In the field of active safety and autonomous driving of Mercedes-Benz Cars, continuous efforts are being made to bring systems into the vehicle that make driving even safer and get closer to our vision of accident-free driving. The continuous improvement of traffic and vehicle safety is an integral part of Mercedes-Benz' DNA. In order to understand the accident and to further improve driving assistance systems, it is helpful to analyze data from the customer fleet with regard to accident-critical driving situations. To this end, it is essential to identify critical driving situations as automated as possible from the internal vehicle data collected (run dynamic parameters as well as visual data from the camera sensor system) and to link them to external data sources such as, for example, map information or accident hotspots. These challenges come to you: The aim of the work is to derive a methodology for categorizing accident-critical scenarios from fleet data of the Mercedes-Benz fleet and to exemplarily validate the methodology on a limited data set (Proof of Concept). Essential building blocks of the final work: Methods for identifying accident-critical driving situations from vehicle and visual data

Detection of parameters for the criticality identification

Development of a methodology for identifying and categorizing critical driving situation

Sample application of the methodology to fleet data

Documentation and presentation of results

The activity can begin from February 2026.

Course in Automotive Engineering, Mechanical Engineering, Computer Science or Comparable

Secure knowledge of German and English in word and writing

Safe handling of MS Office

Commitment and team skills

Analytical thinking and strategic working

Basic experience and expertise in vehicle technology, driving physics and driving assistance systems, as well as AEB, ACC and TTC should not be foreign words for you

Programming and data processing using Python should also include your repertoire

The final topics will be discussed with the university, you and us. Additional information: We are looking forward to your online application with CV, lettering, certificates, current enrollment certificate with an indication of the semester and proof of the regular study period. Please do not forget to mark your documents as ''relevant for this application' in the online form and to observe the maximum file size of 5 MB. Further information on the setting criteria can be found here. Disabled and equalized applicants are welcome! The severely disabled representative (SBV-Sindelfingen@mercedes-benz.com) is happy to support you in the application process. HR Services will be happy to help you with questions about the application process. You can reach us by email via myhrservice@mercedes-benz.com or by phone at 0711/17-99000 (Mo-Fr 10-12am & 13-15am).

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