# Exploiting Sparsity in Automotive Radar Object Detection Networks

Source: [Exploiting Sparsity in Automotive Radar Object Detection Networks](<https://www.sota2.com/research/paper/68fa705b1e209131cea4a927>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:40:08.455Z

Authors: Marius Lippke, Maurice Quach, Sascha Braun, Daniel K\\"ohler, Michael Ulrich, Bastian Bischoff, Wei Yap Tan

Published: 2023-08-15T12:58:06.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2308.07748>)

[PDF](<https://arxiv.org/pdf/2308.07748.pdf>)