Radar Perception with Scalable Connective Temporal Relations for Autonomous Driving

Due to the noise and low spatial resolution in automotive radar data, exploring temporal relations of learnable features over consecutive 2 radar frames has shown performance gain on downstream tasks (e.g., object detection and tracking) in our previous study [1]. In this paper, we further enhance radar perception by significantly extending the time horizon of temporal relations. To this end, we propose a scalable connective temporal radar (SCTR) method that consists of 1) a standard temporal relation layer (TRL), 2) a connective TRL with shifted window attention, and 3) a window merging operation, to facilitate feature connectivity between radar frames over an extended time interval. Our complexity analysis and comprehensive evaluation of the Radiate dataset demonstrate that the SCTR achieves a great tradeoff between the complexity and downstream detection performance.