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.

翻译：精确的环境感知对于确保自动驾驶系统安全可靠运行至关重要。雷达目标检测网络是该类系统的核心组成部分之一。基于CNN的目标检测器在此领域展现出良好性能，但需要消耗大量计算资源。本文研究稀疏卷积目标检测网络，该网络将强大的网格检测能力与低计算资源需求相结合。我们针对雷达特有的挑战展开研究，提出稀疏核点柱体（SKPP）和双体素点卷积（DVPC）作为网格渲染与稀疏主干架构的改进方案。在nuScenes数据集上的评估表明，我们的SKPP-DPVCN架构在Car AP4.0指标上相比基线提升5.89%，较此前最优方法提升4.19%。此外，SKPP-DPVCN将平均尺度误差（ASE）较基线降低21.41%。