For autonomous driving, radar sensors provide superior reliability regardless of weather conditions as well as a significantly high detection range. State-of-the-art algorithms for environment perception based on radar scans build up on deep neural network architectures that can be costly in terms of memory and computation. By processing radar scans as point clouds, however, an increase in efficiency can be achieved in this respect. While Convolutional Neural Networks show superior performance on pattern recognition of regular data formats like images, the concept of convolutions is not yet fully established in the domain of radar detections represented as point clouds. The main challenge in convolving point clouds lies in their irregular and unordered data format and the associated permutation variance. Therefore, we apply a deep-learning based method introduced by PointCNN that weights and permutes grouped radar detections allowing the resulting permutation invariant cluster to be convolved. In addition, we further adapt this algorithm to radar-specific properties through distance-dependent clustering and pre-processing of input point clouds. Finally, we show that our network outperforms state-of-the-art approaches that are based on PointNet++ on the task of semantic segmentation of radar point clouds.

翻译：自动驾驶领域中，雷达传感器在各类天气条件下具有卓越的可靠性以及显著的高探测距离。基于雷达扫描的环境感知前沿算法依赖于深度神经网络架构，这类架构在内存和计算方面成本高昂。然而，通过将雷达扫描处理为点云，可以在这一方面实现效率提升。尽管卷积神经网络在图像等规则数据格式的模式识别中表现出色，但在以点云形式表示的雷达探测领域，卷积的概念尚未完全确立。卷积点云的主要挑战在于其不规则且无序的数据格式以及由此产生的排列可变性。因此，我们采用PointCNN提出的深度学习方法，该方法通过对分组的雷达探测进行加权和排列变换，使产生的排列不变簇能够被卷积。此外，我们进一步通过距离相关聚类和输入点云预处理，使该算法适应雷达特性。最后，我们证明，在雷达点云语义分割任务上，我们的网络性能优于基于PointNet++的前沿方法。