{"type":"ScholarlyArticle","title":"PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird’s-Eye View","authors":[{"name":"Peizheng Li","slug":"peizheng-li","given":"Peizheng","family":"Li","affiliation":"Mercedes-Benz AG , Stuttgart , Germany; University of Tübingen , Tübingen , Germany"},{"name":"Shuxiao Ding","slug":"shuxiao-ding","given":"Shuxiao","family":"Ding","affiliation":"Mercedes-Benz AG , Stuttgart , Germany; University of Bonn , Bonn , Germany"},{"name":"Xieyuanli Chen","slug":"xieyuanli-chen","given":"Xieyuanli","family":"Chen","affiliation":"University of Bonn , Bonn , Germany"},{"name":"Niklas Hanselmann","slug":"niklas-hanselmann","given":"Niklas","family":"Hanselmann","affiliation":"Mercedes-Benz AG , Stuttgart , Germany; University of Tübingen , Tübingen , Germany"},{"name":"Marius Cordts","slug":"marius-cordts","given":"Marius","family":"Cordts","affiliation":"Mercedes-Benz AG , Stuttgart , Germany"},{"name":"Juergen Gall","slug":"juergen-gall","given":"Juergen","family":"Gall","affiliation":"University of Bonn , Bonn , Germany; Lamarr Institute for Machine Learning and Artificial Intelligence , Germany"}],"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.","doi":"10.24963/ijcai.2023/120","url":"https://ijcai.org/proceedings/2023/120","pdf":"https://ijcai.org/proceedings/2023/0120.pdf","markdown":"https://ijcai.org/proceedings/2023/120.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/120","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":120,"pages":{"first":1080,"last":1088},"keywords":["Computer Vision: CV: Segmentation","Computer Vision: CV: Motion and tracking"],"videos":[],"references":[{"position":1,"raw":"[Akan and Güney, 2022] Adil Kaan Akan and Fatma Güney. 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