Learning-Deep-Learning

NEAT: Neural Attention Fields for End-to-End Autonomous Driving

October 2021

tl;dr: transformers to learn an interpretable BEV representation for end-to-end autonomous driving.

Overall impression

The goal of the paper is interpretable, high-performance, end-to-end autonomous driving. Yet the way to generate the interpretable intermediate representation is quite interesting.

Both NEAT and PYVA also uses the idea of transformers in the task of lifting image to BEV, however upon closer look, the transformers are not used specifically for view transformation.

• NEAT uses transformers to enhance the features in image feature space, before using MLP-based iterative attention to lift image features into BEV space.
• PYVA uses MLP to lift image features to BEV space first and then uses a transformer to enhance the features in BEV.
• Both of them are quite different from Tesla’s approach.

The paper has many esoteric details regarding planning and control, and I am not sure if I fully understands those parts, and the discussion of those parts are ignored here.

Key ideas

• Encoder
  • $R^{S \times T \times W \times H \times 3} \times R \rightarrow R^{(STP) \times C}$
  • scalar v (ego car velocity)
  • P=64 patches (input 256x256, @P5)
  • output image feature is c
  • Transformers is used to integrate features globally, adding contexutual cues to each patch with its self-attention mechanism.
  • The transformer can be removed without impacting the output dimension.
• Neural Attention Field (NEAT)
  • $R^5 \times R^C \rightarrow R^{STP}$
  • The 5-dim input p is a query. The C-dim input $c_{i-1}$ is a reduced version of c, and c_0 is initialized as the global mean of c.
  • The output is a spatial-aware attention a_i.
  • $c_i = softmax(a_i)^T \cdot c$.
  • Why $c_{i-1}$ instead of the fully fledged c? The authors argue that NEAT could take into the c directly, but it would be inefficient due to the high dimensionality of c. They opted for an iterative attention instead. The key question is, the channel attention $c_{i-1}$ does not have any spatial information any more, and why can the iterative refining progress generate a spatial-aware attention a_i? If the NEAT does take the fully fledged a directly as input, would one round of iteration be enough?
• Decoder
  • $R^5 \times R^C \rightarrow R^{K} \times R^2$
  • K channel semantic classes, and 2 dense offset prediction

Technical details

• The C-channel features $c_{i-1} \in R^C$ are initiated as the average of $C \in R^{(STP)\times C}$. This should be largely equivalent to the context summary in Tesla’s AI day presentation.
• The MLP in the neural attention field module should have taken p (query, 5) and a fully-fledged image feature c ($STP \times C$). Then it should have $(STP)$ dimension attention map, then the output is a softmax-scaled dot product between c and the attention map. In Tesla’s AI day presentation, the coordConv is replaced with a positional embedding, as is in trasnformers.
  • In this paper, the authors argue that this approach is too inefficient due to the high dimension of c ($STP \times C$). Therefore it average pools c into c_0 ($C$) and feed into the MLP. As the input of the MLP does not have any spatial information regarding the original c (spatial content already reduced to 1x1 by global averaging), it has to use N iterations to refine. In this sense, the NEAT is a simplified version of Transformers. –> Not super sure the rationale behind the N iterations of this approach.
• The MLP in NEAT decoder takes in 5 + C (query + C-channel features) as input, and iterates this on all possible query locations (2) in the BEV space (More precisely, location (x,y) + time + target waypoint location (x’, y’), thus 5 dimensions. But we will ignore the latter 3 here for simplicity). This is equivalent to have a raster of BEV space with CoordConv and tile the globally (average) pooled in BEV space, and perform a 1x1 convolution on top of it. This 1x1 convolution keeps the spatial dimension to the same (BEV h x w) and channel is changed from 5+C to K+2 (K is semantic classes, 2 is offset prediction).
  • This decoder part is not detailed in Tesla’s AI day presentation. And the MLP should be equivalently replaced by a 1x1 convolution. In practice, 3x3 convolutions may be even better in the decoder of BEV features.

Notes

• Code on github