Driven by deep learning techniques, perception technology in autonomous driving has developed rapidly in recent years, enabling vehicles to accurately detect and interpret surrounding environment for safe and efficient navigation. To achieve accurate and robust perception capabilities, autonomous vehicles are often equipped with multiple sensors, making sensor fusion a crucial part of the perception system. Among these fused sensors, radars and cameras enable a complementary and cost-effective perception of the surrounding environment regardless of lighting and weather conditions. This review aims to provide a comprehensive guideline for radar-camera fusion, particularly concentrating on perception tasks related to object detection and semantic segmentation.Based on the principles of the radar and camera sensors, we delve into the data processing process and representations, followed by an in-depth analysis and summary of radar-camera fusion datasets. In the review of methodologies in radar-camera fusion, we address interrogative questions, including "why to fuse", "what to fuse", "where to fuse", "when to fuse", and "how to fuse", subsequently discussing various challenges and potential research directions within this domain. To ease the retrieval and comparison of datasets and fusion methods, we also provide an interactive website: https://radar-camera-fusion.github.io.

翻译：在深度学习技术的驱动下，自主驾驶中的感知技术近年来发展迅速，使车辆能够精确探测并解析周围环境，从而实现安全高效的导航。为实现精确且鲁棒的感知能力，自主车辆通常配备多种传感器，使得传感器融合成为感知系统的关键组成部分。在这些融合传感器中，雷达与摄像头能够以低成本实现不受光照和天气条件影响的互补性环境感知。本综述旨在为雷达-摄像头融合提供一份全面的指南，特别关注物体检测与语义分割相关的感知任务。基于雷达与摄像头传感器的原理，我们深入探讨了数据处理流程与表征方式，随后对雷达-摄像头融合数据集进行了深入分析与总结。在雷达-摄像头融合方法的综述中，我们阐述了融合的动机、融合内容、融合位置、融合时机及融合方式等关键问题，并进一步讨论了该领域内的各类挑战与潜在研究方向。为便于数据集与融合方法的检索与比较，我们还提供了一个交互式网站：https://radar-camera-fusion.github.io。