Problem
Research questions and friction points this paper is trying to address.
end-to-end driving
BEV representation
pretraining
sparse targets
autonomous driving
Planning-Aligned Pretraining of BEV Representations with Sparse Action-Conditioned Targets for End-to-End Autonomous Driving
🤖 AI Summary
本文提出PAVER方法，通过单次LiDAR扫描构建稀疏目标来预训练BEV表示，以改善端到端自动驾驶的规划相关性，无需密集重建或任务注释。
📝 Abstract
End-to-end driving requires planning-relevant bird's-eye-view (BEV) representations, but existing pretraining approaches often rely on task annotations or dense scene reconstruction. We introduce PAVER, Planning-Aligned BEV Encoder Pretraining. From a single LiDAR sweep, PAVER constructs sparse risk and unknown targets describing occupied and unobserved evidence along rule-based ego motions. A 10K-parameter head predicts these targets from masked BEV features conditioned on the action state, directing supervision toward geometric constraints on candidate motions. Pretraining requires no driving-task annotations or dense reconstruction. Only the BEV encoder is transferred, preserving the downstream architecture and camera-only inference. On nuScenes, PAVER reduces VAD-Tiny's average collision rate from 0.51% to 0.19%, while improving planning L2, motion prediction, detection, and mapping. The selected VAD-Tiny and VAD-Base schedules use about 36% less estimated total training time than scratch training, including pretraining. On Bench2Drive Town05 Long, PAVER improves UniAD-Tiny's closed-loop Driving Score from 48.45 to 58.79. The project page is available at https://archiiive99.github.io/PAVER.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Planning-Aligned BEV Encoder Pretraining
Sparse Action-Conditioned Targets
End-to-end Autonomous Driving
LiDAR Sweep
Geometric Constraints
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2024-09-26Citations: 1
