计算机工程与应用2025，Vol.61Issue(6)：1-21,21.DOI:10.3778/j.issn.1002-8331.2407-0501
BEV感知学习在自动驾驶中的应用综述
Review of Application of BEV Perceptual Learning in Autonomous Driving
摘要
Abstract
As the types of sensors used as acquisition inputs in the autonomous driving perception module continue to develop,it becomes more and more difficult to represent the multi-modal data uniformly.BEV perception learning in the automatic driving perception task module can make multi-modal data unified integration into a feature space,which has better development potential compared with other perception learning models.The reasons for the good development potential of BEV perception model are summarized from five aspects:research significance,spatial deployment,prepara-tion work,algorithm development,and evaluation index.The BEV perception model can be summarized into four series from a framework perspective:Lift-Splat-Lss series,IPM reverse perspective conversion,MLP view conversion and Transformer view conversion.The input data can be summarized into two categories:the first type of pure image feature input includes monocular camera input and multi-camera input;the second type of fusion data input is not only the simple data fusion of point cloud data and image features,but also the knowledge distillation fusion guided or supervised by point cloud data and the fusion of height segmentation by guided slice.It provides an overview of the application of four kinds of automatic driving tasks in BEV perception model,such as multi-target tracking,map segmentation,lane detection and 3D target detection,and summarizes the shortcomings of the four series of current BEV perception learning frameworks.关键词
BEV感知学习/视图转换/多模态数据融合/多目标追踪/地图分割/车道线检测及3D目标检测Key words
BEV perception learning/view conversion/multi-modal data fusion/multi-target tracking/map segmenta-tion/lane detection and 3D target detection分类
计算机与自动化引用本文复制引用
黄德启,黄海峰,黄德意,刘振航..BEV感知学习在自动驾驶中的应用综述[J].计算机工程与应用,2025,61(6):1-21,21.基金项目
新疆维吾尔自治区自然科学基金(2022D01C430) （2022D01C430）
国家自然科学基金(51468062). （51468062）
