FY26 Master Thesis - BEV Coherent Synthetic Image Degradation Data & BEV Degradation Robustness Ben

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General Summary:

Background:

For autonomous driving, multiple cameras are needed to create a full view of the surround vehicle. In an autonomous driving system, it is critical to be robust to degradations. To overcome this challenge, one could teach the system to account for degradations by including data with degradations in training. The difficulty with this is to collect the data because the phenomena are rare, and large-scale collection is not feasible. However, creating realistic synthetic data is an alternative solution.

Purpose:

This thesis explores the creation of synthetic image degradation data, specifically focusing on windshield and lens artifacts such as blur, blockage, and other related artifacts, aiming to create realistic image degradation across all sensors. Potential methods could be to use Generative Adversarial Networks (GANs) or diffusion models to generate synthetic images.

The study also aims to evaluate the robustness of BEV methods under the influence of image degradation, including the synthetically generated data.

Student Background:

• Proficiency in programming languages such as Python.
• Courses in image processing, computer vision, and machine learning.
• Experience image processing libraries (e.g., TensorFlow, PyTorch, OpenCV).
• Experience with GANs (beneficial)

*References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies.

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