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。

自动驾驶和高级驾驶辅助系统的摄像头 - 雷达感知：概念，数据集和度量

Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics

Video-based person re-identification (Re-ID) aims at matching the video
tracklets with cropped video frames for identifying the pedestrians under
different cameras. However, there exists severe spatial and temporal
misalignment for those cropped tracklets due to the imperfect detection and
tracking results generated with obsolete methods. To address this issue, we
present a simple re-Detect and Link (DL) module which can effectively reduce
those unexpected noise through applying the deep learning-based detection and
tracking on the cropped tracklets. Furthermore, we introduce an improved model
called Coarse-to-Fine Axial-Attention Network (CF-AAN). Based on the typical
Non-local Network, we replace the non-local module with three 1-D
position-sensitive axial attentions, in addition to our proposed coarse-to-fine
structure. With the developed CF-AAN, compared to the original non-local
operation, we can not only significantly reduce the computation cost but also
obtain the state-of-the-art performance (91.3% in rank-1 and 86.5% in mAP) on
the large-scale MARS dataset. Meanwhile, by simply adopting our DL module for
data alignment, to our surprise, several baseline models can achieve better or
comparable results with the current state-of-the-arts. Besides, we discover the
errors not only for the identity labels of tracklets but also for the
evaluation protocol for the test data of MARS. We hope that our work can help
the community for the further development of invariant representation without
the hassle of the spatial and temporal alignment and dataset noise. The code,
corrected labels, evaluation protocol, and the aligned data will be available
at this https URL

提出了一种基于深度学习检测和追踪的重新检测和链接模块，并结合粗到细轴向注意网络优化了视频行人重识别，可以显著降低计算成本并在 MARS 数据集上实现排名 1 的最高表现，能够解决由于不完美的检测和跟踪结果导致的空间和时间错配问题，同时发现了数据集中的噪声和评估协议错误，为是次领域研究提供了可靠的数据和基础工具。