  Accurately detecting lane lines in 3D space is crucial for autonomous
driving. Existing methods usually first transform image-view features into
bird-eye-view (BEV) by aid of inverse perspective mapping (IPM), and then
detect lane lines based on the BEV features. However, IPM ignores the changes
in road height, leading to inaccurate view transformations. Additionally, the
two separate stages of the process can cause cumulative errors and increased
complexity. To address these limitations, we propose an efficient transformer
for 3D lane detection. Different from the vanilla transformer, our model
contains a decomposed cross-attention mechanism to simultaneously learn lane
and BEV representations. The mechanism decomposes the cross-attention between
image-view and BEV features into the one between image-view and lane features,
and the one between lane and BEV features, both of which are supervised with
ground-truth lane lines. Our method obtains 2D and 3D lane predictions by
applying the lane features to the image-view and BEV features, respectively.
This allows for a more accurate view transformation than IPM-based methods, as
the view transformation is learned from data with a supervised cross-attention.
Additionally, the cross-attention between lane and BEV features enables them to
adjust to each other, resulting in more accurate lane detection than the two
separate stages. Finally, the decomposed cross-attention is more efficient than
the original one. Experimental results on OpenLane and ONCE-3DLanes demonstrate
the state-of-the-art performance of our method.
