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.
@article{arxiv.2303.07586,
title = {Teacher-Student Knowledge Distillation for Radar Perception on Embedded Accelerators},
author = {Steven Shaw and Kanishka Tyagi and Shan Zhang},
journal= {arXiv preprint arXiv:2303.07586},
year = {2023}
}