Neural Architecture Search for Bird-Eye-View Perception Models for Automotive Applications
- Typ:Masterarbeit
- Datum:ab 04 / 2025
- Betreuung:
Neural Architecture Search for Bird-Eye-View Perception Models for Automotive Applications
Context
Neural Architecture Search (NAS) has been a powerful tool for automating the design of DL models by optimizing the architectures of the NNs achieving better efficiency and performance. Meanwhile, Bird’s-Eye-View (BEV) perception models [1] have become essential in automotive applications, enabling robust scene understanding for autonomous driving and Advanced Driver-Assistance Systems (ADAS). This thesis will utilize NAS to BEV perception models aiming to find architectures that can be used in embedded systems.
Targets
- In this work state-of-the-art (SOTA) in NAS and BEV approaches will be researched.
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Based on these results a NAS framework tailored for searching optimized BEV architectures will be designed and implemented.
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The performance of discovered models will be evaluated using datasets for autonomous driving on the embedded GPU platforms and compared to SOTA models.
Requirements
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Experience with Deep Learning (DL) and one of the DL Frameworks like PyTorch or Keras/Tensorflow
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Experience with Python and Computer Vision