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.

翻译：雷达因其低成本和对恶劣天气的良好适应性而在自动驾驶系统中无处不在。然而，由于方位角和仰角分辨率较低，雷达点云稀疏且不准确，因而其检测性能通常较差。此外，现有的点云生成算法会丢弃弱信号以减少虚假目标，这可能导致深度融合的效果欠佳。在本文中，我们提出了一种名为EchoFusion的新方法，该方法跳过现有的雷达信号处理流程，将雷达原始数据与其他传感器进行融合。具体而言，我们首先生成鸟瞰图（BEV）查询，然后从雷达中提取相应的频谱特征，以与其他传感器融合。通过这种方式，我们的方法能够利用雷达回波中丰富且无损耗的距离和速度线索，以及图像中丰富的语义线索，从而在RADIal数据集上超越所有现有方法，并接近激光雷达的性能。代码将在论文接收后公开。