Exploiting Sparsity in Automotive Radar Object Detection Networks
Marius Lippke Maurice Quach Sascha Braun Daniel Köhler Michael Ulrich Bastian Bischoff Wei Yap Tan
Having precise perception of the environment is crucial for ensuring the
secure and reliable functioning of autonomous driving systems. Radar object
detection networks are one fundamental part of such systems. CNN-based object
detectors showed good performance in this context, but they require large
compute resources. This paper investigates sparse convolutional object
detection networks, which combine powerful grid-based detection with low
compute resources. We investigate radar specific challenges and propose sparse
kernel point pillars (SKPP) and dual voxel point convolutions (DVPC) as
remedies for the grid rendering and sparse backbone architectures. We evaluate
our SKPP-DPVCN architecture on nuScenes, which outperforms the baseline by
5.89% and the previous state of the art by 4.19% in Car AP4.0. Moreover,
SKPP-DPVCN reduces the average scale error (ASE) by 21.41% over the baseline.