Master Thesis Development of an ML-Based Post-Processing Scene Classification for ADAS Development Using Continuously Evaluated Data

Company Description

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

The Robert Bosch GmbH is looking forward to your application!

Job Description

In recent years, Advanced Driver Assistance Systems (ADAS) such as Adaptive Cruise Control (ACC) and lane departure warning have become increasingly popular. However, these systems still have room for improvement in certain scenarios, which requires a systematic search for such scenes during the development phase. Unfortunately, this process requires expensive and time-consuming manual expert labelling to cluster and label these effects.

• To address this problem, in your Master thesis you will design a machine learning algorithm using continuous-valued input data from radar and camera to classify faulty scenarios in post-processing.
• Furthermore, you will test and optimize the algorithm using real data.
• Last but not least, you will integrate the algorithm into an existing validation framework.

Qualifications

• Education: Master studies in the field of Engineering or comparable
• Experience and Knowledge: in Python; skills in Deep Learning (Pytorch), driver assistance systems and sensors (radar, camera) are an advantage
• Personality and Working Practice: anindependent, motivated and reliable person
• Languages: very good in English and basic in German 

Additional Information

Start: according to prior agreement
Duration: 6 months

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

Need further information about the job?
Patricia Duran (Functional Department)
+49 711 811 37308

#LI-DNI

Summary

• Type: Full-time
• Function: Engineering