Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review
Shanliang Yao, Runwei Guan, Zitian Peng, Chenhang Xu, Yilu Shi,, Weiping Ding, Eng Gee Lim, Yong Yue, Hyungjoon Seo, Ka Lok Man, Jieming Ma,, Xiaohui Zhu, Yutao Yue

TL;DR
This paper reviews various radar data representations used in autonomous driving, analyzing their principles, datasets, advantages, limitations, and future research directions to improve perception systems.
Contribution
It provides a comprehensive analysis of five radar data representations, their generation processes, and discusses challenges and future research avenues in autonomous driving.
Findings
Different radar representations have unique advantages and limitations.
The review highlights key datasets and methods for radar perception.
Challenges include data diversity and representation robustness.
Abstract
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.…
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Taxonomy
TopicsAdvanced SAR Imaging Techniques · Radar Systems and Signal Processing · Advanced Optical Sensing Technologies
