Many radar signal processing methodologies are being developed for critical road safety perception tasks. Unfortunately, these signal processing algorithms are often poorly suited to run on embedded hardware accelerators used in automobiles. Conversely, end-to-end machine learning (ML) approaches better exploit the performance gains brought by specialized accelerators. In this paper, we propose a teacher-student knowledge distillation approach for low-level radar perception tasks. We utilize a hybrid model for stationary object detection as a teacher to train an end-to-end ML student model. The student can efficiently harness embedded compute for real-time deployment. We demonstrate that the proposed student model runs at speeds 100x faster than the teacher model.

翻译：目前正在为关键的道路安全认知任务制定许多雷达信号处理方法,不幸的是,这些信号处理算法往往不适合在汽车中使用的嵌入式硬件加速器上运行。相反,端到端机器学习(ML)方法可以更好地利用专门加速器带来的绩效收益。在本文中,我们建议对低级雷达认知任务采用师生知识蒸馏方法。我们使用固定物体探测混合模型作为师资来培训终端到终端ML学生模型。学生可以有效地利用嵌入式计算器实时部署。我们证明,拟议的学生模型运行速度比教师模型快100倍。</s>