  In the field of autonomous driving, 3D object detection is a very important
perception module. Although the current SOTA algorithm combines Camera and
Lidar sensors, limited by the high price of Lidar, the current mainstream
landing schemes are pure Camera sensors or Camera+Radar sensors. In this study,
we propose a new detection algorithm called HVDetFusion, which is a multi-modal
detection algorithm that not only supports pure camera data as input for
detection, but also can perform fusion input of radar data and camera data. The
camera stream does not depend on the input of Radar data, thus addressing the
downside of previous methods. In the pure camera stream, we modify the
framework of Bevdet4D for better perception and more efficient inference, and
this stream has the whole 3D detection output. Further, to incorporate the
benefits of Radar signals, we use the prior information of different object
positions to filter the false positive information of the original radar data,
according to the positioning information and radial velocity information
recorded by the radar sensors to supplement and fuse the BEV features generated
by the original camera data, and the effect is further improved in the process
of fusion training. Finally, HVDetFusion achieves the new state-of-the-art
67.4\% NDS on the challenging nuScenes test set among all camera-radar 3D
object detectors. The code is available at
https://github.com/HVXLab/HVDetFusion
