Radar-based perception has gained increasing attention in autonomous driving,
yet the inherent sparsity of radars poses challenges. Radar raw data often
contains excessive noise, whereas radar point clouds retain only limited
information. In this work, we holistically treat the sparse nature of radar
data by introducing an adaptive subsampling method together with a tailored
network architecture that exploits the sparsity patterns to discover global and
local dependencies in the radar signal. Our subsampling module selects a subset
of pixels from range-doppler (RD) spectra that contribute most to the
downstream perception tasks. To improve the feature extraction on sparse
subsampled data, we propose a new way of applying graph neural networks on
radar data and design a novel two-branch backbone to capture both global and
local neighbor information. An attentive fusion module is applied to combine
features from both branches. Experiments on the RADIal dataset show that our
SparseRadNet exceeds state-of-the-art (SOTA) performance in object detection
and achieves close to SOTA accuracy in freespace segmentation, meanwhile using
sparse subsampled input data.

基于稀疏雷达数据的前瞻感知在自动驾驶中越来越受到关注，本论文通过引入自适应子采样方法和定制化的网络架构来综合处理稀疏雷达数据的特性，并利用稀疏模式发现雷达信号中的全局和局部相关性。实验证明，我们的 SparseRadNet 在目标检测方面超过了最先进的水平，并在自由空间分割方面几乎达到了最先进的准确性，同时使用了稀疏子采样输入数据。

SparseRadNet: 基于子采样雷达数据的稀疏感知神经网络

SparseRadNet: Sparse Perception Neural Network on Subsampled Radar Data

Radars, due to their robustness to adverse weather conditions and ability to
measure object motions, have served in autonomous driving and intelligent
agents for years. However, Radar-based perception suffers from its unintuitive
sensing data, which lack of semantic and structural information of scenes. To
tackle this problem, camera and Radar sensor fusion has been investigated as a
trending strategy with low cost, high reliability and strong maintenance. While
most recent works explore how to explore Radar point clouds and images, rich
contextual information within Radar observation are discarded. In this paper,
we propose a hybrid point-wise Radar-Optical fusion approach for object
detection in autonomous driving scenarios. The framework benefits from dense
contextual information from both the range-doppler spectrum and images which
are integrated to learn a multi-modal feature representation. Furthermore, we
propose a novel local coordinate formulation, tackling the object detection
task in an object-centric coordinate. Extensive results show that with the
information gained from optical images, we could achieve leading performance in
object detection (97.69\% recall) compared to recent state-of-the-art methods
FFT-RadNet (82.86\% recall). Ablation studies verify the key design choices and
practicability of our approach given machine generated imperfect detections.
The code will be available at this https URL

本文提出一种混合逐点雷达 - 光学融合方法，结合了来自范围 - 多普勒频谱和光学图像的密集上下文信息，用于自动驾驶场景中的目标检测，表现优于最近的 FFT-RadNet 方法。

ROFusion: 利用混合点云雷达 - 光学融合实现高效物体检测

ROFusion: Efficient Object Detection using Hybrid Point-wise  Radar-Optical Fusion

One of the main paths towards the reduction of traffic accidents is the
increase in vehicle safety through driver assistance systems or even systems
with a complete level of autonomy. In these types of systems, tasks such as
obstacle detection and segmentation, especially the Deep Learning-based ones,
play a fundamental role in scene understanding for correct and safe navigation.
Besides that, the wide variety of sensors in vehicles nowadays provides a rich
set of alternatives for improvement in the robustness of perception in
challenging situations, such as navigation under lighting and weather adverse
conditions. Despite the current focus given to the subject, the literature
lacks studies on radar-based and radar-camera fusion-based perception. Hence,
this work aims to carry out a study on the current scenario of camera and
radar-based perception for ADAS and autonomous vehicles. Concepts and
characteristics related to both sensors, as well as to their fusion, are
presented. Additionally, we give an overview of the Deep Learning-based
detection and segmentation tasks, and the main datasets, metrics, challenges,
and open questions in vehicle perception.

本文研究了自动驾驶系统中车辆安全性的提高、深度学习基础的障碍物检测和分割、雷达传感器和摄像头传感器的融合，以及在车辆感知中的数据集、度量、挑战和开放性问题，这些元素对于提高自动驾驶系统的普及率具有重要意义。