  Environmental perception with the multi-modal fusion of radar and camera is
crucial in autonomous driving to increase accuracy, completeness, and
robustness. This paper focuses on utilizing millimeter-wave (MMW) radar and
camera sensor fusion for 3D object detection. A novel method that realizes the
feature-level fusion under the bird's-eye view (BEV) for a better feature
representation is proposed. Firstly, radar points are augmented with temporal
accumulation and sent to a spatial-temporal encoder for radar feature
extraction. Meanwhile, multi-scale image 2D features which adapt to various
spatial scales are obtained by image backbone and neck model. Then, image
features are transformed to BEV with the designed view transformer. In
addition, this work fuses the multi-modal features with a two-stage fusion
model called point-fusion and ROI-fusion, respectively. Finally, a detection
head regresses objects category and 3D locations. Experimental results
demonstrate that the proposed method realizes the state-of-the-art (SOTA)
performance under the most crucial detection metrics-mean average precision
(mAP) and nuScenes detection score (NDS) on the challenging nuScenes dataset.
