Most automated driving systems comprise a diverse sensor set, including
several cameras, Radars, and LiDARs, ensuring a complete 360\deg coverage in
near and far regions. Unlike Radar and LiDAR, which measure directly in 3D,
cameras capture a 2D perspective projection with inherent depth ambiguity.
However, it is essential to produce perception outputs in 3D to enable the
spatial reasoning of other agents and structures for optimal path planning. The
3D space is typically simplified to the BEV space by omitting the less relevant
Z-coordinate, which corresponds to the height dimension.The most basic approach
to achieving the desired BEV representation from a camera image is IPM,
assuming a flat ground surface. Surround vision systems that are pretty common
in new vehicles use the IPM principle to generate a BEV image and to show it on
display to the driver. However, this approach is not suited for autonomous
driving since there are severe distortions introduced by this too-simplistic
transformation method.

自动驾驶系统的感知输出对于其他智能体和结构的空间推理和路径规划至关重要，基于摄像头图像的透视投影方法不适用于自动驾驶，因此需要使用其他方法来产生所需的地形俯视图像。