Among various sensors for assisted and autonomous driving systems, automotive radar has been considered as a robust and low-cost solution even in adverse weather or lighting conditions. With the recent development of radar technologies and open-sourced annotated data sets, semantic segmentation with radar signals has become very promising. However, existing methods are either computationally expensive or discard significant amounts of valuable information from raw 3D radar signals by reducing them to 2D planes via averaging. In this work, we introduce ERASE-Net, an Efficient RAdar SEgmentation Network to segment the raw radar signals semantically. The core of our approach is the novel detect-then-segment method for raw radar signals. It first detects the center point of each object, then extracts a compact radar signal representation, and finally performs semantic segmentation. We show that our method can achieve superior performance on radar semantic segmentation task compared to the state-of-the-art (SOTA) technique. Furthermore, our approach requires up to 20x less computational resources. Finally, we show that the proposed ERASE-Net can be compressed by 40% without significant loss in performance, significantly more than the SOTA network, which makes it a more promising candidate for practical automotive applications.

翻译：在辅助驾驶和自动驾驶系统的各类传感器中，汽车雷达即便在恶劣天气或光照条件下仍被视为鲁棒且低成本的选择。随着近年来雷达技术的发展以及开源标注数据集的涌现，基于雷达信号的语义分割方法展现出广阔前景。然而现有方法要么计算代价高昂，要么通过平均化处理将原始3D雷达信号降维至2D平面，从而丢弃了大量有价值信息。本研究提出ERASE-Net（高效雷达分割网络），旨在对原始雷达信号进行语义分割。其核心创新在于针对原始雷达信号的新颖"先检测后分割"方法：首先检测每个物体的中心点，继而提取紧凑的雷达信号表征，最后执行语义分割。实验表明，与现有最优技术相比，本方法在雷达语义分割任务中性能更优，且计算资源消耗降低至原来的1/20。此外，所提出的ERASE-Net可在性能无明显损失的情况下压缩40%，压缩幅度显著超过现有最优网络，使其成为更具前景的实际车载应用候选方案。