PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View

• URL: http://arxiv.org/abs/2306.10761v1
• Date: Mon, 19 Jun 2023 08:11:05 GMT
• Title: PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View
• Authors: Peizheng Li, Shuxiao Ding, Xieyuanli Chen, Niklas Hanselmann, Marius Cordts and Juergen Gall
• Abstract summary: Bird's-eye view (BEV) representations are commonplace in perception for autonomous driving. Existing approaches for BEV instance prediction rely on a multi-task auto-regressive coupled with post-processing to predict future instances. We 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.
• Score: 14.113805629254191
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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