Remote sensing technologies such as Radar and Lidar are commonly used in the fields of Advanced Driver Assistance Systems (ADAS), Automated Driving (AD) and robotics. Specifically, in combination with the Frequency Modulated Continuous Wave (FMCW) principle they play a vital role in determining position and radial velocity of surrounding objects. However, both “detection and ranging” technologies offer specific advantages in certain sensing scenarios when it comes to resolution, range and resilience in various weather and lighting conditions.
The aim of this project is to exploit these advantages and develop a collaborative sensing scheme for exchange of information among the sensor technologies. This will include the design, implementation and evaluation of machine learning (ML) based sensor fusion algorithms with the aim of increasing the overall perception performance of the system while limiting power consumption to a level that can be handled by resource-constrained mobile processing platforms.
The Institute of Visual Computing at the Graz University of Technology contributes to the project with training resources and expertise in the areas of Artificial Intelligence, Machine Learning, and Automated Driving.
