Fusion Point Pruning for Optimized 2D Object Detection with Radar-Camera Fusion

Abstract

Object detection is one of the most important perception tasks for advanced driver assistant systems and autonomous driving. Due to its complementary features and moderate cost, radar-camera fusion is of particular interest in the automotive industry but comes with the challenge of how to optimally fuse the heterogeneous data sources. To solve this for 2D object detection, we propose two new techniques to project the radar detections onto the image plane, exploiting additional uncertainty information. We also introduce a new technique called fusion point pruning, which automatically finds the best fusion points of radar and image features in the neural network architecture. These new approaches combined surpass the state of the art in 2D object detection performance for radar-camera fusion models, evaluated with the nuScenes dataset. We further find that the utilization of radar-camera fusion is especially beneficial for night scenes.