With the rapid advancements of sensor technology and deep learning,
autonomous driving systems are providing safe and efficient access to
intelligent vehicles as well as intelligent transportation. Among these
equipped sensors, the radar sensor plays a crucial role in providing robust
perception information in diverse environmental conditions. This review focuses
on exploring different radar data representations utilized in autonomous
driving systems. Firstly, we introduce the capabilities and limitations of the
radar sensor by examining the working principles of radar perception and signal
processing of radar measurements. Then, we delve into the generation process of
five radar representations, including the ADC signal, radar tensor, point
cloud, grid map, and micro-Doppler signature. For each radar representation, we
examine the related datasets, methods, advantages and limitations. Furthermore,
we discuss the challenges faced in these data representations and propose
potential research directions. Above all, this comprehensive review offers an
in-depth insight into how these representations enhance autonomous system
capabilities, providing guidance for radar perception researchers. To
facilitate retrieval and comparison of different data representations, datasets
and methods, we provide an interactive website at
this https URL

该综述研究了自动驾驶系统中使用的雷达数据表示，介绍了雷达传感器的能力和局限性，探讨了 ADC 信号、雷达张量、点云、网格地图和微多普勒特征等五种雷达数据表示方法，并分析了相关数据集、方法、优势和局限性，提出了潜在的研究方向和挑战，为雷达感知研究者提供了深入洞察，并通过交互网站方便不同数据表示、数据集和方法的检索和比较。

自动驾驶中的雷达感知：探索不同的数据表示

Radar Perception in Autonomous Driving: Exploring Different Data  Representations

In multimodal traffic monitoring, we gather traffic statistics for distinct
transportation modes, such as pedestrians, cars and bicycles, in order to
analyze and improve people's daily mobility in terms of safety and convenience.
On account of its robustness to bad light and adverse weather conditions, and
inherent speed measurement ability, the radar sensor is a suitable option for
this application. However, the sparse radar data from conventional commercial
radars make it extremely challenging for transportation mode classification.
Thus, we propose to use a high-resolution millimeter-wave(mmWave) radar sensor
to obtain a relatively richer radar point cloud representation for a traffic
monitoring scenario. Based on a new feature vector, we use the multivariate
Gaussian mixture model (GMM) to do the radar point cloud segmentation, i.e.
`point-wise' classification, in an unsupervised learning environment. In our
experiment, we collected radar point clouds for pedestrians and cars, which
also contained the inevitable clutter from the surroundings. The experimental
results using GMM on the new feature vector demonstrated a good segmentation
performance in terms of the intersection-over-union (IoU) metrics. The detailed
methodology and validation metrics are presented and discussed.

以毫米波雷达为传感器，提出基于高分辨率雷达点云的混合高斯模型无监督学习算法，在多模态交通监测中取得了良好的分割效果。