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：自动驾驶领域对雷达传感器的兴趣再度升温。作为一项相对成熟的技术，雷达在过去数年间持续优化升级，使其成为广泛应用的激光雷达的理想替代或补充方案。当前新兴趋势是利用丰富的低层级雷达数据进行感知。本研究将这一趋势推向极致——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可学习的信号处理模块，并引入基于传统信号处理算法的预训练机制。实验验证了端到端学习方法的整体有效性，消融研究则证实了各项创新设计的实际效果。