Vision Language Action (VLA) models show strong performance for data-driven autonomous driving using latest AI technology. As VLA systems combine LLMs for text input, computer vision encoder and complex action generation, the system often consume considerable resources and memory. A recent line of research is focusing on more efficient VLA models, mainly by optimizing how the different modules VLAs interact.
This master’s thesis focuses on evaluation, benchmarking and enhancing of VLA models which focus on efficiency The project involves systematic evaluation of different VLA systems and optimization techniques on realistic benchmarks.
Key Research Areas:
- Review of VLA models for autonomous driving regarding efficiency
- Quantitative evaluation of VLA systems and features regarding inference efficiency (time, memory)
- (optional) Enhancements of VLA systems for inference, integrating or combining different mechanisms for efficiency improvement
This position is only for students enrolled in a university looking for a Master Thesis. For students at TUM in München, thesis supervision from university side can be provided. For others, this needs to be agreed with their home university.
- Currently enrolled in a computer science / engineering Master studies with a focus on data analysis, automotive, engineering or similar
- Interest in autonomous driving and machine learning
- Basic understanding of deep learning and model training
- Ability to read and modify research codebases
- Proficiency in Python
- Experience with VLA is a plus
- Home office: Possibility to work from home
- Health: Yearly health & wellbeing days, Urban Sports Club membership
- Attractive working environment: Modern offices, canteen onsite and free parking
- Open company culture: Diverse, multicultural team
- Events: Regular company & team events
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