Papers › Exploiting Temporal Relations on Radar Perception for Autonomous Driving

Exploiting Temporal Relations on Radar Perception for Autonomous Driving

3 Apr 2022CVPR 2022 1arXiv:2204.01184archive 2025-07-28

Peizhao Li, Pu Wang, Karl Berntorp, Hongfu Liu

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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Tasks

2D Object DetectionAutonomous DrivingMultiple Object TrackingObjectObject DetectionObject RecognitionObject Trackingobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
2D Object Detection RADIATE TempoRadar mAP@0.3 63.63±2.08 #2 of 2 Archive leaderboard report
Multiple Object Tracking RADIATE TempoRadar MOTA 37.91 #2 of 2 Archive leaderboard report

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