  Accurately perceiving instances and predicting their future motion are key
tasks for autonomous vehicles, enabling them to navigate safely in complex
urban traffic. While bird's-eye view (BEV) representations are commonplace in
perception for autonomous driving, their potential in a motion prediction
setting is less explored. Existing approaches for BEV instance prediction from
surround cameras rely on a multi-task auto-regressive setup coupled with
complex post-processing to predict future instances in a spatio-temporally
consistent manner. In this paper, we depart from this paradigm and propose an
efficient novel end-to-end framework named POWERBEV, which differs in several
design choices aimed at reducing the inherent redundancy in previous methods.
First, rather than predicting the future in an auto-regressive fashion,
POWERBEV uses a parallel, multi-scale module built from lightweight 2D
convolutional networks. Second, we show that segmentation and centripetal
backward flow are sufficient for prediction, simplifying previous multi-task
objectives by eliminating redundant output modalities. Building on this output
representation, we propose a simple, flow warping-based post-processing
approach which produces more stable instance associations across time. Through
this lightweight yet powerful design, POWERBEV outperforms state-of-the-art
baselines on the NuScenes Dataset and poses an alternative paradigm for BEV
instance prediction. We made our code publicly available at:
https://github.com/EdwardLeeLPZ/PowerBEV.
