Thesis in Multi-Modal Predictions for End-to-End Architectures
Thesis in Multi-Modal Predictions for End-to-End Architectures
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Job Description
In the context of autonomous driving the need for accurate predictions is paramount. Achieving more robust predictions is crucial for ensuring the safety and efficiency of autonomous vehicles. By accurately predicting the future movements of surrounding objects and other vehicles autonomous driving systems can proactively plan and adapt their actions thereby reducing the risk of accidents and improving overall driving experience. Additionally the ability to provide feedback from predictions back to perception allows the system to continuously learn and improve its overall endtoend capabilities leading to enhanced decisionmaking in dynamic environments.
- During your thesis you will address the critical need for predictions in autonomous driving systems. To this end you will aim to improve the coupling between predictions and perception (i.e. multiobject detection and tracking).
- Querybased approaches have been receiving a lot of attention during the last years particularly for their performance and easy integration within endtoend architectures. Current architectures fatigue to have a proper flow of information from downstream tasks to the perception models of the architecture. Within this thesis you will explore methods for achieving this also considering multimodal predictions models.
Qualifications :
- Education: studies in the field of Computer Science or comparable
- Experience and Knowledge: in data analysis and visualization; proficiency in Python and tools like Pandas NumPy and Matplotlib; knowledge of deep learning frameworks; familiarity with TensorFlow Keras or PyTorch; understanding of computer vision; experience with OpenCV or similar libraries; familiarity with autonomous systems (e.g. automated driving)
- Personality and Working Practice: you are eager to learn and able to tackle complex challenges develop innovative solutions clearly articulate technical concepts to both technical and nontechnical audiences
- Languages: fluent in English
Additional Information :
Start: according to prior agreement
Duration: 6 months
Requirement for this thesis is the enrollment at university. Please attach your CV transcript of records examination regulations and if indicated a valid work and residence permit.
You are almost finished with your Bachelors degree and would like to gain some practical experience before embarking on your next academic adventure with a Masters degree Then you fit in perfectly well with our PreMaster Programm! Take a look at our vacancies here.
Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore we welcome all applications regardless of gender age disability religion ethnic origin or sexual identity.
Need further information about the job
Joerg Wagner (Functional Department)
49 0
#LIDNI
Remote Work :
No
Employment Type :
Fulltime
Employment Type
Full-time
