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

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, 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.

Key words: BEV perception learning, view conversion, multi-modal data fusion, multi-target tracking, map segmentation, lane detection and 3D target detection