Papers › NEAT: Neural Attention Fields for End-to-End Autonomous Driving
NEAT: Neural Attention Fields for End-to-End Autonomous Driving
Kashyap Chitta, Aditya Prakash, Andreas Geiger
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 representation. This allows our model to selectively attend to relevant regions in the input while ignoring information irrelevant to the driving task, effectively associating the images with the BEV representation. In a new evaluation setting involving adverse environmental conditions and challenging scenarios, NEAT outperforms several strong baselines and achieves driving scores on par with the privileged CARLA expert used to generate its training data. Furthermore, visualizing the attention maps for models with NEAT intermediate representations provides improved interpretability.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Autonomous Driving | CARLA Leaderboard | NEAT | Driving Score | 21.83 | #15 of 18 | Archive leaderboard | report |
| Autonomous Driving | CARLA Leaderboard | NEAT | Infraction penalty | 0.65 | #15 of 18 | Archive leaderboard | report |
| Autonomous Driving | CARLA Leaderboard | NEAT | Route Completion | 41.71 | #15 of 18 | Archive leaderboard | report |
| CARLA longest6 | CARLA | Neural Attention Fields (NEAT) | Driving Score | 24 | #19 of 21 | Archive leaderboard | report |
| CARLA longest6 | CARLA | Neural Attention Fields (NEAT) | Infraction Score | 0.71 | #19 of 21 | Archive leaderboard | report |
| CARLA longest6 | CARLA | Neural Attention Fields (NEAT) | Route Completion | 62 | #19 of 21 | Archive leaderboard | report |
| Novel View Synthesis | X3D | NeAT | PSNR | 36.01 | #4 of 6 | Archive leaderboard | report |
| Novel View Synthesis | X3D | NeAT | SSIM | 0.9638 | #4 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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