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

作　　者：黄德启[1] 黄海峰 黄德意 刘振航 HUANG Deqi;HUANG Haifeng;HUANG Deyi;LIU Zhenhang(School of Electrical Engineering,Xinjiang University,Urumqi 830017,China)

机构地区：[1]新疆大学电气工程学院,乌鲁木齐830017

出　　处：《计算机工程与应用》2025年第6期1-21,共21页Computer Engineering and Applications

基　　金：新疆维吾尔自治区自然科学基金(2022D01C430);国家自然科学基金(51468062)。

摘　　要：自动驾驶感知模块中作为采集输入的传感器种类不断发展,要使多模态数据统一地表征出来变得愈加困难。BEV感知学习在自动驾驶感知任务模块中可以使多模态数据统一融合到一个特征空间,相比于其他感知学习模型拥有更好的发展潜力。从研究意义、空间部署、准备工作、算法发展及评价指标五个方面总结了BEV感知模型具有良好发展潜力的原因。BEV感知模型从框架角度概括为四个系列:Lift-Splat-Lss系列、IPM逆透视转换、MLP视图转换及Transformer视图转换;从输入数据概括为两类:第一类是纯图像特征的输入包括单目摄像头输入和多摄像头输入,第二类在融合数据输入中不仅是简单的点云数据和图像特征的数据融合,还包括了以点云数据为引导或监督的知识蒸馏融合和以引导切片方式去划分高度段的融合。概述了多目标追踪、地图分割、车道线检测及3D目标检测四种自动驾驶任务在BEV感知模型当中的应用,并总结了目前BEV感知学习四个系列框架的缺点。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,preparation 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 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]