  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. The code is available at
https://github.com/TabGuigui/FipTR .
