Master Thesis Improving Object Detection Performance of Automotive Radar by Deeply Reinforced Cognitive Sensing

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 GmbHis looking forward to your application!

Job Description

We are looking for a Master thesis student to contribute to the improvement of object detection performance in automotive radar systems. This research aims to address the challenges of detecting weak targets and vulnerable road users at long distances and in complex object constellations, overcoming the limitations of current static sensor modulations.

• During your Master thesis, you will implement cognitive sensing to adapt sensor modulation based on sensor results and their uncertainties to optimise radar object detection capabilities.
• You will extend the existing online radar simulation framework to create a closed-loop pipeline for reinforcement learning.
• Furthermore, you will train a reinforcement learning agent to optimise sensor modulation parameters under different conditions and driving scenarios.
• In addition, you will perform regression tests and ablation studies to evaluate the performance of the approach.
• Finally, you will benchmark the cognitive sensing approach against baseline methods in different use cases.

Qualifications

• Education: Master studies in the field of Electrical Engineering, Computer Engineering, Cybernetics or comparable
• Experience and Knowledge: profound knowledge of machine learning and radar technologies; coding experience in Python and deep learning frameworks, ideally PyTorch
• Personality and Working Practice: an independent, systematic and analytical person
• Languages: fluent in English; German is a plus

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?
Marius Schwarz (Functional Department)
+49 173 576 5310
Sherif Abdulatif (Functional Department)
+49 711 811 14364

#LI-DNI

Summary

• Type: Full-time
• Function: Research