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RaSS: 4D mm-Wave Radar Point Cloud Semantic Segmentation with Cross-Modal Knowledge Distillation.

Chenwei Zhang1, Zhiyu Xiang1,2, Ruoyu Xu1

  • 1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.

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Summary
This summary is machine-generated.

This study explores 4D mm-Wave radar for semantic segmentation in autonomous driving. The proposed RaSS framework, using cross-modal distillation and Doppler compensation, significantly improves point-level perception, even with sparse radar data.

Keywords:
knowledge distillationradarsemantic segmentation

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Area of Science:

  • Robotics and Artificial Intelligence
  • Sensor Fusion for Autonomous Systems
  • Environmental Perception Technologies

Background:

  • Autonomous driving relies on sensors like LiDAR and cameras for environmental perception.
  • 4D mm-Wave radar offers robust performance in adverse weather, capturing 3D point clouds and Doppler velocities.
  • Radar's inherent data sparsity and noise limit its application in point-level tasks like semantic segmentation.

Purpose of the Study:

  • To investigate the feasibility of utilizing 4D mm-Wave radar for semantic segmentation tasks.
  • To develop a novel framework for radar-based semantic segmentation.
  • To address the challenges posed by sparse and noisy radar data.

Main Methods:

  • Introduction of the ZJUSSet dataset, providing point-wise class labels for radar and LiDAR data.
  • Proposal of RaSS, a cross-modal distillation framework designed for radar semantic segmentation.
  • Development of an adaptive Doppler compensation module to enhance segmentation accuracy.

Main Results:

  • The RaSS model demonstrated significant performance improvements over existing baselines and competitors on the ZJUSSet and VoD datasets.
  • The framework effectively handles sparse and noisy 4D radar data for semantic segmentation.
  • Validation of 4D radar's potential for detailed environmental perception tasks.

Conclusions:

  • 4D mm-Wave radar is a viable sensor for semantic segmentation in autonomous driving, overcoming previous limitations.
  • The RaSS framework and adaptive Doppler compensation module represent a significant advancement in radar perception.
  • Future work will involve releasing the code and dataset to facilitate further research in this area.