Exploiting Temporal Relations on Radar Perception for Autonomous Driving
- URL: http://arxiv.org/abs/2204.01184v1
- Date: Sun, 3 Apr 2022 23:52:25 GMT
- Title: Exploiting Temporal Relations on Radar Perception for Autonomous Driving
- Authors: Peizhao Li, Pu Wang, Karl Berntorp, Hongfu Liu
- Abstract summary: We exploit the temporal information from successive ego-centric bird-eye-view radar image frames for radar object recognition.
We propose a temporal relational layer to explicitly model the relations between objects within successive radar images.
- Score: 26.736501544682294
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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.
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