The task of autonomous driving demands vehicles to create a precise understanding of their surroundings. Currently, camera, LiDAR, and radar sensors are used to perceive the vehicle’s environment. While conventional automotive radar sensors have the advantage of instantly detecting velocities and are less prone to disturbances by difficult weather conditions than the aforementioned alternatives, they also offer a lower resolution and therefore less information for classification and tracking tasks. 
To compensate for this weakness, the usage of newly available polarimetric radar sensors for the automotive domain is researched. Polarimetric radar sensors emit and detect radar waves with different polarizations. The analysis of polarization changes adds additional information about reflection patterns, allowing, inter alia, estimation of vehicle orientation and extend in traffic scenarios. 
Prior research also indicates improvements in classification tasks using polarimetric radar data of stationary targets in test areas  and stationary and moving traffic participants in real-world urban scenarios . In this work, polarimetric radar data is used to track and predict the evolution of the vehicle’s environment over time.
The thesis consists of the following milestones:
- Implement and compare optical flow methods to create correlation images from polarimetric and conventional radar imagery
- Implement and compare track generation methods using the correlation images
- Implement and compare track prediction methods using the generated tracks
- Evaluate advantages of polarimetric radar information
 Tristan Visentin.Polarimetric Radar for Automotive Applications, volume 90 ofKarlsruher Forschungs-berichte aus dem Institut f ̈ur Hochfrequenztechnik und Elektronik. KIT Scientific Publishing, Karlsruhe,Baden, 2019.
 J. F. Tilly, F. Weishaupt, O. Schumann, J. Dickmann, and G. Wanielik. Road user classification with polarimetric radars. In2020 17th European Radar Conference (EuRAD), pages 112–115, 2021.