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.

翻译：随着传感器技术和深度学习的快速发展，自动驾驶系统为智能车辆及智能交通提供了安全高效的接入方式。在所配备的传感器中，雷达传感器在多样化环境条件下提供稳健的感知信息方面发挥着关键作用。本文综述聚焦于探索自动驾驶系统中使用的不同雷达数据表示。首先，我们通过考察雷达感知的工作原理及雷达测量的信号处理过程，介绍了雷达传感器的能力与局限性。随后，深入探讨了五种雷达表示的生成过程，包括ADC信号、雷达张量、点云、栅格地图和微多普勒特征。针对每种雷达表示，我们研究了相关数据集、方法、优势及局限性。此外，我们讨论了这些数据表示面临的挑战，并提出了潜在的研究方向。最重要的是，本综合评述深入洞察了这些表示如何增强自动驾驶系统能力，为雷达感知研究者提供指导。为便于检索和比较不同数据表示、数据集及方法，我们在https://radar-camera-fusion.github.io/radar提供了交互式网站。