This challenge awaits you:
In the fault response test, electrical diagnoses of the drive control units are validated by means of targeted fault activation. The entire chain of events in the vehicle is analysed – from fault detection to the response of the overall system. The key questions in the evaluation are as follows:
1. Are the correct faults in the fault memory?
2. Are there additional faults in the fault memory that should not have occurred?
3. Are the correct replacement responses (e.g. emergency mode) initiated?
4. Are the correct error messages triggered?
5. Are the repair instructions for the workshop correct?
6. Is communication with the other control units correct?
A specially developed web application provides support during test execution and evaluation. The generated data is stored in a database and pre-processed in the web application using evaluation scripts. The test results generated by the scripts are then evaluated manually and forwarded accordingly if any anomalies are found.
The aim of this thesis is to analyse historical error data and target response behaviour using AI methods in order to be able to automatically evaluate vehicle responses and test results in future tests. The aim is to identify patterns and develop models that enable intelligent evaluation of the response chain.
Your Tasks:
7. You will conduct extensive research into literature on possible AI techniques for automated evaluation.
8. You will analyse historical error data, test results and target responses from vehicle fleets from past tests.
9. You are responsible for developing and implementing AI methods for the automated evaluation of error response tests, including the development of a model for predicting the target behaviour of the vehicle.
10. You derive optimisation potential for further AI-supported automation.
11. You document the results and create reports for the department.
Necessary Skills:
12. Current studies in computer science, information science, electrical engineering or a comparable degree programme
13. Good programming skills (Python, C/C++, or similar)
14. Initial practical experience with deep learning frameworks (PyTorch, tf, or similar)
15. German language skills at C1 level and English language skills at B2 level
Desired Skills:
16. Analytical mindset and ability to work independently
17. High level of commitment, creativity, willingness to learn and motivation
Das spricht für uns:
Als Student:in arbeitest du bei IAV nicht irgendwo, sondern mittendrin. In echten Projekten. An spannenden Zukunftsaufgaben. Voll integriert und im Schulterschluss mit IAV-Expert:innen. Viel Verantwortung und gleichzeitig viel Freiraum, um Uni und Arbeit zusammen zu bringen: So entstehen beste Perspektiven für deine berufliche Entwicklung. Bei attraktiver Vergütung nach unserem Haustarifvertrag.
Uns sind Vielfalt und Chancengleichheit wichtig. Für uns zählt der Mensch mit seiner Persönlichkeit und seinen Stärken.