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

@article{Chitta2021NEATNA,
  title={NEAT: Neural Attention Fields for End-to-End Autonomous Driving},
  author={Kashyap Chitta and Aditya Prakash and Andreas Geiger},
  journal={2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021},
  pages={15773-15783}
}
Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel representation that enables such reasoning for end-to-end imitation learning models. NEAT is a continuous function which maps locations in Bird’s Eye View (BEV) scene coordinates to waypoints and semantics, using intermediate attention maps to iteratively compress high-dimensional 2D image features into a compact… 

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