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With the rapid advancements of sensor technology and deep learning,autonomous driving systems are providing safe and efficient access tointelligent vehicles as well as intelligent transportation. Among theseequipped sensors, the radar sensor plays a crucial role in providing robustperception information in diverse environmental conditions. This review focuseson exploring different radar data representations utilized in autonomousdriving systems. Firstly, we introduce the capabilities and limitations of theradar sensor by examining the working principles of radar perception and signalprocessing of radar measurements. Then, we delve into the generation process offive radar representations, including the ADC signal, radar tensor, pointcloud, grid map, and micro-Doppler signature. For each radar representation, weexamine the related datasets, methods, advantages and limitations. Furthermore,we discuss the challenges faced in these data representations and proposepotential research directions. Above all, this comprehensive review offers anin-depth insight into how these representations enhance autonomous systemcapabilities, providing guidance for radar perception researchers. Tofacilitate retrieval and comparison of different data representations, datasetsand methods, we provide an interactive website athttps://radar-camera-fusion.github.io/radar.

Radar Perception in Autonomous Driving: Exploring Different Data Representations