{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T16:33:39Z","timestamp":1783096419602,"version":"3.54.6"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>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\u2019s-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.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/120","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:31:30Z","timestamp":1691742690000},"page":"1080-1088","source":"Crossref","is-referenced-by-count":31,"title":["PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird\u2019s-Eye View"],"prefix":"10.24963","author":[{"given":"Peizheng","family":"Li","sequence":"first","affiliation":[{"name":"Mercedes-Benz AG"},{"name":"University of T\u00fcbingen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuxiao","family":"Ding","sequence":"additional","affiliation":[{"name":"Mercedes-Benz AG"},{"name":"University of Bonn"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xieyuanli","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Bonn"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niklas","family":"Hanselmann","sequence":"additional","affiliation":[{"name":"Mercedes-Benz AG"},{"name":"University of T\u00fcbingen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marius","family":"Cordts","sequence":"additional","affiliation":[{"name":"Mercedes-Benz AG"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juergen","family":"Gall","sequence":"additional","affiliation":[{"name":"University of Bonn"},{"name":"Lamarr Institute for Machine Learning and Artificial Intelligence"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","theme":"Artificial Intelligence","location":"Macau, SAR China","acronym":"IJCAI-2023","number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2023,8,19]]},"end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:36:23Z","timestamp":1691742983000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/120"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/120","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}