Radar Perception in Autonomous Driving: Exploring Different Data
  Representations
Shanliang Yao Runwei Guan Zitian Peng Chenhang Xu Yilu Shi Yong Yue Eng Gee Lim Hyungjoon Seo Ka Lok Man Xiaohui Zhu Yutao Yue
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
https://radar-camera-fusion.github.io/radar.