RadSegNet: A Reliable Approach to Radar Camera Fusion

Abstract

Perception systems for autonomous driving have seen significant advancements in their performance over last few years. However, these systems struggle to show robustness in extreme weather conditions because sensors like lidars and cameras, which are the primary sensors in a sensor suite, see a decline in performance under these conditions. In order to solve this problem, cameraradar fusion systems provide a unique opportunity for all weather reliable high quality perception. Cameras provides rich semantic information while radars can work through occlusions and in all weather conditions. In this work we show that the state-of-the-art fusion methods perform poorly when camera input is degraded, which essentially results in losing the all-weather reliability they set out to achieve. Contrary to these approaches, we propose a new method, RadSeg-Net, that uses a new design philosophy of independent information extraction and truly achieves reliability in all conditions including occlusions and adverse weather. We develop and validate our proposed system on the benchmark Astyx dataset and further verify these results on the RADIATE dataset. When compared to state-of-the-art methods, RadSegNet achieves a 27% improvement on Astyx and 41.46% increase on RADIATE, in average precision score and maintains a significantly better performance in adverse weather conditions.

Point Cloud Painting for 3D Object Detection with Camera and Automotive 3+1D RADAR Fusion

“…A remarkable network that opted for a data-level approach is RadSegNet [ 12 ]. The method uses a 2D detector for oriented bounding boxes.…”