Kavli Affiliate: Feng Wang
| First 5 Authors: Yang Liu, Feng Wang, Naiyan Wang, Zhaoxiang Zhang,
| Summary:
Radar is ubiquitous in autonomous driving systems due to its low cost and
good adaptability to bad weather. Nevertheless, the radar detection performance
is usually inferior because its point cloud is sparse and not accurate due to
the poor azimuth and elevation resolution. Moreover, point cloud generation
algorithms already drop weak signals to reduce the false targets which may be
suboptimal for the use of deep fusion. In this paper, we propose a novel method
named EchoFusion to skip the existing radar signal processing pipeline and then
incorporate the radar raw data with other sensors. Specifically, we first
generate the Bird’s Eye View (BEV) queries and then take corresponding spectrum
features from radar to fuse with other sensors. By this approach, our method
could utilize both rich and lossless distance and speed clues from radar echoes
and rich semantic clues from images, making our method surpass all existing
methods on the RADIal dataset, and approach the performance of LiDAR. Codes
will be available upon acceptance.
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