  With the rapid advancement of autonomous driving technology, there is a
growing need for enhanced safety and efficiency in the automatic environmental
perception of vehicles during their operation. In modern vehicle setups,
cameras and mmWave radar (radar), being the most extensively employed sensors,
demonstrate complementary characteristics, inherently rendering them conducive
to fusion and facilitating the achievement of both robust performance and
cost-effectiveness. This paper focuses on a comprehensive survey of
radar-vision (RV) fusion based on deep learning methods for 3D object detection
in autonomous driving. We offer a comprehensive overview of each RV fusion
category, specifically those employing region of interest (ROI) fusion and
end-to-end fusion strategies. As the most promising fusion strategy at present,
we provide a deeper classification of end-to-end fusion methods, including
those 3D bounding box prediction based and BEV based approaches. Moreover,
aligning with recent advancements, we delineate the latest information on 4D
radar and its cutting-edge applications in autonomous vehicles (AVs). Finally,
we present the possible future trends of RV fusion and summarize this paper.
