ADCNet: End-to-end perception with raw radar ADC data

Abstract: 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.

Arxiv Preprint

Bibtex:

@misc{yang2023adcnet,
      title={ADCNet: End-to-end perception with raw radar ADC data},
      author={Bo Yang and Ishan Khatri and Michael Happold and Chulong Chen},
      year={2023},
      eprint={2303.11420},
      archivePrefix={arXiv},
      primaryClass={eess.SP}
}