Radar Based Dynamic Occupancy Grid

Radar-based dynamic occupancy grid mapping creates real-time representations of environments, distinguishing between static and moving objects using radar data. Current research focuses on improving the accuracy of these maps, particularly in differentiating dynamic objects, through techniques like deep learning-based state correction and advanced particle filtering methods tailored to radar's unique characteristics. This work is significant for advancing autonomous vehicle perception, offering a robust and potentially cost-effective alternative to lidar-based systems by leveraging the increasing capabilities of radar sensors. Improved radar-based mapping contributes to safer and more reliable autonomous navigation.