Occupancy mapping for automotive applications

Intelligent vehicles are equipped with multiple sensors, such as automotive radar, LiDAR, and camera, that collect an enormous amount of data about the environment around them. Interpreting the data and obtaining a meaningful representation of the environment is a challenging problem. The goal of the project is to design algorithms that fuse and convert the multimodal sensor data into a useful map for the driver. In particular, the project aims to exploit the underlying physical properties of the data, such as spatial correlation, redundancy, multimodality, etc.

Assignment

In this MSc project, you will develop new sparsity-driven algorithms and design grid occupancy mapping for automotive applications.

Requirements

For this project, we are looking for a master's student in either electrical engineering or any related study. Furthermore, we are looking for a student who has a background in signal processing, sensors, basic statistical techniques, data analysis, and programming skills in Matlab, Python, and/or C/C++. Strong communication (written and verbal) skills in English are mandatory. The expected project duration is about 9 to 12 months.

Contact

Last modified: 2024-03-04