The future instance prediction from a Bird's Eye View(BEV) perspective is a
vital component in autonomous driving, which involves future instance
segmentation and instance motion prediction. Existing methods usually rely on a
redundant and complex pipeline which requires multiple auxiliary outputs and
post-processing procedures. Moreover, estimated errors on each of the auxiliary
predictions will lead to degradation of the prediction performance. In this
paper, we propose a simple yet effective fully end-to-end framework named
Future Instance Prediction Transformer(FipTR), which views the task as BEV
instance segmentation and prediction for future frames. We propose to adopt
instance queries representing specific traffic participants to directly
estimate the corresponding future occupied masks, and thus get rid of complex
post-processing procedures. Besides, we devise a flow-aware BEV predictor for
future BEV feature prediction composed of a flow-aware deformable attention
that takes backward flow guiding the offset sampling. A novel future instance
matching strategy is also proposed to further improve the temporal coherence.
Extensive experiments demonstrate the superiority of FipTR and its
effectiveness under different temporal BEV encoders.

本文提出了一种名为 FipTR 的简单而有效的全面端到端框架，它将任务视为 BEV 实例分割和未来帧预测，并采用表示特定交通参与者的实例查询来直接估计相应的未来占用掩码，从而摆脱了复杂的后处理过程。此外，我们设计了一种适用于未来 BEV 特征预测的流感知 BEV 预测器，其由一个以反向流为指导进行偏移采样的流感知可变形关注组成。还提出了一种新颖的未来实例匹配策略，进一步改善了时间上的一致性。大量实验证明了 FipTR 的优越性以及其在不同时间 BEV 编码器下的有效性。