A Survey of Deep Learning Based Radar and Vision Fusion for 3D Object Detection in Autonomous Driving
It addresses the need for enhanced safety and efficiency in autonomous driving perception by reviewing fusion techniques, but it is incremental as it is a survey paper summarizing existing methods.
This paper provides a comprehensive survey of deep learning-based radar and vision fusion methods for 3D object detection in autonomous driving, covering categories like ROI and end-to-end fusion, including 3D bounding box and BEV approaches, and discussing recent advancements such as 4D radar applications.
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.