Fusing mmWave Radar With Camera for 3-D Detection in Autonomous Driving

Abstract

Three-dimensional detection is essential for autonomous driving and intelligent transportation system, as it enables vehicles to detect and track surrounding objects. Usually, autonomous vehicles are equipped with multiple sensing modalities to achieve robust and precise detection. This work focuses on fusing millimeter-wave radar data with monocular images, as radar can make up for the lack of explicit depth information. We propose a novel approach that fuses radar data and images at the feature level for 3-D detection. Radar points are first merged into a raw feature map with data set statistics by a novel transformation method. With this transformation, radar features can be extracted by convolutional neural networks and fused with image features. Object properties, including location, dimension, and rotation are regressed from the fused features. In this article, the proposed fusion strategy is implemented with a keypoint-based 3-D detection framework and evaluated on the challenging NuScenes data set. Experimental results suggest that the fusion of radar data promotes 3-D detection capability in public benchmarking.