There is a renewed interest in radar sensors in the autonomous driving industry. As a relatively mature technology, radars have seen steady improvement over the last few years, making them an appealing alternative or complement to the commonly used LiDARs. An emerging trend is to leverage rich, low-level radar data for perception. In this work we push this trend to the extreme -- we propose a method to perform end-to-end learning on the raw radar analog-to-digital (ADC) data. Specifically, we design a learnable signal processing module inside the neural network, and a pre-training method guided by traditional signal processing algorithms. Experiment results corroborate the overall efficacy of the end-to-end learning method, while an ablation study validates the effectiveness of our individual innovations.

翻译：自动驾驶行业对雷达传感器的兴趣重燃。作为一种相对成熟的技术，近年来雷达性能稳步提升，使其成为广泛使用的激光雷达的极具吸引力的替代或补充方案。利用丰富、低层级雷达数据进行感知正成为新兴趋势。本研究将该趋势推向极致——提出一种在原始雷达模数转换数据上进行端到端学习的方法。具体而言，我们在神经网络内部设计了可学习的信号处理模块，并开发了基于传统信号处理算法的预训练方法。实验证实了端到端学习方法的整体有效性，消融研究则验证了各项创新点的独立效能。