TR2022-069

Exploiting Temporal Relations on Radar Perception for Autonomous Driving


    •  Li, P., Pu, W., Berntorp, K., Liu, H., "Exploiting Temporal Relations on Radar Perception for Autonomous Driving", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2022.
      BibTeX TR2022-069 PDF
      • @inproceedings{Li2022jun,
      • author = {Li, Peizhao and Pu, Wang and Berntorp, Karl and Liu, Hongwu},
      • title = {Exploiting Temporal Relations on Radar Perception for Autonomous Driving},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2022,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2022-069}
      • }
  • MERL Contacts:
  • Research Areas:

    Communications, Computational Sensing, Optimization, Signal Processing

Abstract:

We consider the object recognition problem in autonomous driving using automotive radar sensors. Comparing to Lidar sensors, radar is cost-effective and robust in all-weather conditions for perception in autonomous driving. However, radar signals suffer from low angular resolution and precision in recognizing surrounding objects. To enhance the capacity of automotive radar, in this work, we exploit the temporal information from successive ego-centric bird-eye-view radar image frames for radar object recognition. We leverage the consistency of an object’s existence and attributes (size, orientation, etc.), and propose a temporal relational layer to explicitly model the relations between objects within successive radar images. In both object detection and multiple object tracking, we show the superiority of our method compared to several baseline approaches

 

  • Related Publication

  •  Li, P., Pu, W., Berntorp, K., Liu, H., "Exploiting Temporal Relations on Radar Perception for Autonomous Driving", arXiv, April 2022.
    BibTeX arXiv
    • @article{Li2022apr,
    • author = {Li, Peizhao and Pu, Wang and Berntorp, Karl and Liu, Hongwu},
    • title = {Exploiting Temporal Relations on Radar Perception for Autonomous Driving},
    • journal = {arXiv},
    • year = 2022,
    • month = apr,
    • url = {https://arxiv.org/abs/2204.01184}
    • }