Master Thesis in Sensor Fusion for End-to-End Autonomous Driving Systems

Are you ready to work with cutting-edge deep learning based end-to-end approaches that learn driving behavior directly from sensor data? While these models scale effectively, their robustness in rare and safety critical scenarios remains a key challenge, especially when integrating new sensor modalities. With this thesis, you will have the opportunity to improve performance and reliability by combining prior knowledge with novel sensor inputs.

• You will explore how new sensor information can be integrated into end-to-end architectures and vision-language-action (VLA) models using explicit text-based or latent representations.
• As part of the thesis, you will simulate complex scenarios to train and evaluate the driving models.
• In addition, you will conduct in-depth evaluation and comparison of the developed approaches.

Do you have experience in Spark?, Do you have a Master's degree?, * Education: master studies in the field of Computer Science, Robotics, Applied Mathematics or comparable, with good grades

• Experience and Knowledge: strong theoretical understanding of deep learning principles combined with hands-on implementation; proficiency in Python and familiarity with deep learning frameworks such as PyTorch
• Personality and Working Practice: you bring an open-minded and innovative perspective, communicate clearly, work independently, and approach new topics with curiosity
• Work Routine: your on-site presence is required
• Languages: fluent in English

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