TY - JOUR
T1 - Exploring Radar Data Representations in Autonomous Driving
T2 - A Comprehensive Review
AU - Yao, Shanliang
AU - Guan, Runwei
AU - Peng, Zitian
AU - Xu, Chenhang
AU - Shi, Yilu
AU - Ding, Weiping
AU - Lim, Eng Gee
AU - Yue, Yong
AU - Seo, Hyungjoon
AU - Man, Ka Lok
AU - Ma, Jieming
AU - Zhu, Xiaohui
AU - Yue, Yutao
N1 - Publisher Copyright:
© 2000-2011 IEEE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - autonomous driving
KW - data representations
KW - intelligent transportation
KW - intelligent vehicles
KW - Radar perception
UR - http://www.scopus.com/inward/record.url?scp=105002585998&partnerID=8YFLogxK
U2 - 10.1109/TITS.2025.3554781
DO - 10.1109/TITS.2025.3554781
M3 - Review article
AN - SCOPUS:105002585998
SN - 1524-9050
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
ER -