Autonomous driving systems and advanced driver assistance systems require a description of their surrounding environment. One of the sensors that contributes to this task is Radar. Radar pre-processing and its interface towards autonomous drive applications rely heavily on statistical signal processing methods, some of which are computationally complex and heavy to run. Autonomous drive applications should run in complex scenarios in real-time and have access to limited computational budget. Therefore, looking for tools that can accomplish better performance than previous methods while reduce the computational burden of radar processing chain and its customer applications are of high interest and relevance to the field of autonomous drive.
The purpose of this thesis is to explore new tools using machine learning and deep learning to alleviate radar customer applications of the underlying heavy processing needed to process and use radar data.
In this master thesis project, you will focus on:
- Perform a literature study on different deep learning based methods for radar processing.
- Implement, train and evaluate a deep neural net that can detect objects based on radar data.
- Document the results and lessons learned.
We are looking for 2 students with an interest in deep learning for autonomous driving. The following skills would be highly valuable:
- Python programming
- Machine learning
- Reading scientific papers
- Handling large datasets
Having had deep learning related courses and some hands-on experience with these methods is a plus.
Please send in individual applications with CV, motivational letter and grade transcripts.
Planned start: January 2022, with some flexibility.
Final application date: 30th of November 2021
Duration: 30 ECTS
For questions regarding the project, please contact: firstname.lastname@example.org, email@example.com