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Master Thesis Improving Object Detection Performance of Automotive Radar by Deeply Reinforced Cognitive Sensing
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Aktualität: 12.09.2024
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12.09.2024, Bosch-Gruppe
Renningen
Master Thesis Improving Object Detection Performance of Automotive Radar by Deeply Reinforced Cognitive Sensing
Aufgaben:
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.
Qualifikationen:
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
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Master Thesis Improving Object Detection Performance of Automotive Radar by Deeply Reinforced Cognitive Sensing
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