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：针对关键道路安全感知任务，目前正开发多种雷达信号处理方法。遗憾的是，这些信号处理算法通常难以在汽车中使用的嵌入式硬件加速器上高效运行。相比之下，端到端机器学习方法能更好地利用专用加速器带来的性能提升。本文针对低层级雷达感知任务，提出一种师生知识蒸馏方法。我们采用用于静止目标检测的混合模型作为教师模型，训练端到端机器学习的学生模型。该学生模型能够高效利用嵌入式计算资源实现实时部署。实验证明，所提出的学生模型运行速度比教师模型快100倍。