  In this paper, we propose a novel self-supervised motion estimator for
LiDAR-based autonomous driving via BEV representation. Different from usually
adopted self-supervised strategies for data-level structure consistency, we
predict scene motion via feature-level consistency between pillars in
consecutive frames, which can eliminate the effect caused by noise points and
view-changing point clouds in dynamic scenes. Specifically, we propose
\textit{Soft Discriminative Loss} that provides the network with more
pseudo-supervised signals to learn discriminative and robust features in a
contrastive learning manner. We also propose \textit{Gated Multi-frame Fusion}
block that learns valid compensation between point cloud frames automatically
to enhance feature extraction. Finally, \textit{pillar association} is proposed
to predict pillar correspondence probabilities based on feature distance, and
whereby further predicts scene motion. Extensive experiments show the
effectiveness and superiority of our \textbf{ContrastMotion} on both scene flow
and motion prediction tasks. The code is available soon.
