  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.
