Survey of radar-camera fusion for object detection and segmentation in autonomous driving
Survey of radar-camera fusion for object detection and segmentation in autonomous driving
Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review
arXiv paper abstract https://arxiv.org/abs/2304.10410
arXiv PDF paper https://arxiv.org/pdf/2304.10410.pdf
Project page https://xjtlu-vec.github.io/Radar-Camera-Fusion
Driven by deep learning techniques, perception technology in autonomous driving has developed rapidly in recent years.
... autonomous vehicles are often equipped with multiple sensors, making sensor fusion a crucial part of the perception system.
... 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.
... delve into the data processing process and representations, followed by an in-depth analysis and summary of radar-camera fusion datasets.
... 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 ...
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