The unique properties of radar sensors, such as their robustness to adverse
weather conditions, make them an important part of the environment perception
system of autonomous vehicles. One of the first steps during the processing of
radar point clouds is often the detection of clutter, i.e. erroneous points
that do not correspond to real objects. Another common objective is the
semantic segmentation of moving road users. These two problems are handled
strictly separate from each other in literature. The employed neural networks
are always focused entirely on only one of the tasks. In contrast to this, we
examine ways to solve both tasks at the same time with a single jointly used
model. In addition to a new augmented multi-head architecture, we also devise a
method to represent a network's predictions for the two tasks with only one
output value. This novel approach allows us to solve the tasks simultaneously
with the same inference time as a conventional task-specific model. In an
extensive evaluation, we show that our setup is highly effective and
outperforms every existing network for semantic segmentation on the RadarScenes
dataset.

我们研究了一种新的多头神经网络架构，同时解决了雷达点云中的杂波检测和移动道路用户的语义分割问题，并通过只使用一个输出值来表示网络的预测结果，从而达到了与传统特定任务模型相同的推断时间，我们在 RadarScenes 数据集上的广泛评估中证明了我们的设置非常有效，并且在雷达场景的语义分割上优于现有的任何网络。

汽车雷达数据中移动物体的同时杂波检测和语义分割

Simultaneous Clutter Detection and Semantic Segmentation of Moving  Objects for Automotive Radar Data

We present a new and effective approach for Hyperspectral Image (HSI)
classification and clutter detection, overcoming a few long-standing challenges
presented by HSI data characteristics. Residing in a high-dimensional spectral
attribute space, HSI data samples are known to be strongly correlated in their
spectral signatures, exhibit nonlinear structure due to several physical laws,
and contain uncertainty and noise from multiple sources. In the presented
approach, we generate an adaptive, structurally enriched representation
environment, and employ the locally linear embedding (LLE) in it. There are two
structure layers external to LLE. One is feature space embedding: the HSI data
attributes are embedded into a discriminatory feature space where
spatio-spectral coherence and distinctive structures are distilled and
exploited to mitigate various difficulties encountered in the native
hyperspectral attribute space. The other structure layer encloses the ranges of
algorithmic parameters for LLE and feature embedding, and supports a
multiplexing and integrating scheme for contending with multi-source
uncertainty. Experiments on two commonly used HSI datasets with a small number
of learning samples have rendered remarkably high-accuracy classification
results, as well as distinctive maps of detected clutter regions.

利用本文提出的新型方法进行高光谱图像分类与杂波检测，采用了局部线性嵌入（LLE）和结构丰富的表示环境。在两个外部结构层的帮助下，通过特征空间嵌入和参数范围调整，有效克服了高光谱数据本身的不确定性和噪声问题。实验结果表明，该方法可以在样本数量较少的情况下，获得显着的高精度分类结果，以及检测出杂波区域的特征图。