{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T19:26:19Z","timestamp":1773775579102,"version":"3.50.1"},"reference-count":41,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T00:00:00Z","timestamp":1685318400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T00:00:00Z","timestamp":1685318400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,29]]},"DOI":"10.1109\/icra48891.2023.10160984","type":"proceedings-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T17:20:56Z","timestamp":1688491256000},"page":"7801-7807","source":"Crossref","is-referenced-by-count":69,"title":["GoRela: Go Relative for Viewpoint-Invariant Motion Forecasting"],"prefix":"10.1109","author":[{"given":"Alexander","family":"Cui","sequence":"first","affiliation":[{"name":"Waabi, University of Toronto"}]},{"given":"Sergio","family":"Casas","sequence":"additional","affiliation":[{"name":"Waabi, University of Toronto"}]},{"given":"Kelvin","family":"Wong","sequence":"additional","affiliation":[{"name":"Waabi, University of Toronto"}]},{"given":"Simon","family":"Suo","sequence":"additional","affiliation":[{"name":"Waabi, University of Toronto"}]},{"given":"Raquel","family":"Urtasun","sequence":"additional","affiliation":[{"name":"Waabi, University of Toronto"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00886"},{"key":"ref35","article-title":"Tenet: Transformer encoding network for effective temporal flow on motion prediction","author":"wang","year":"2022","journal-title":"ar Xiv preprint"},{"key":"ref12","article-title":"Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets","author":"chou","year":"2019","journal-title":"ArXiv e-prints"},{"key":"ref34","first-page":"203","article-title":"Multimodal trajectory prediction conditioned on lane-graph traversals","author":"deo","year":"2022","journal-title":"Conference on Robot Learning"},{"key":"ref15","article-title":"Implicit latent variable model for scene-consistent motion forecasting","author":"casas","year":"2020","journal-title":"ECCV 2020"},{"key":"ref37","article-title":"Attention is all you need","volume":"30","author":"vaswani","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9196697"},{"key":"ref36","article-title":"Wayformer: Motion forecasting via simple & efficient attention networks","author":"nayakanti","year":"2022","journal-title":"ArXiv Preprint"},{"key":"ref31","first-page":"15303","article-title":"Densetnt: End-to-end trajectory pre-diction from dense goal sets","author":"gu","year":"2021","journal-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision"},{"key":"ref30","article-title":"Tnt: Target-driven trajectory prediction","author":"zhao","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00865"},{"key":"ref33","article-title":"Trajectory forecasts in unknown environments conditioned on grid-based plans","author":"deo","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref10","article-title":"Intentnet: Learning to predict intention from raw sensor data","author":"casas","year":"2018","journal-title":"Conference on Robot Learning"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC48978.2021.9564944"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01154"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/IV51971.2022.9827091"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01417"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01580"},{"key":"ref38","first-page":"20","article-title":"Graph attention networks","volume":"1050","author":"velickovic","year":"2017","journal-title":"Stat"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9341392"},{"key":"ref18","article-title":"Dsdnet: Deep structured self-driving network","author":"zeng","year":"2020","journal-title":"ECCV"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01662"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9196697"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9341199"},{"key":"ref25","author":"jia","year":"2022","journal-title":"Hdgt Heterogeneous driving graph transformer for multi-agent trajectory prediction via scene encoding"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9812107"},{"key":"ref41","first-page":"1321","article-title":"On calibration of modern neural networks","author":"guo","year":"2017","journal-title":"International Conference on Machine Learning"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3146300"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9812253"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00246"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_47"},{"key":"ref29","first-page":"627","article-title":"A reduction of imitation learning and structured prediction to no-regret online learning","author":"ross","year":"2011","journal-title":"Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics"},{"key":"ref8","article-title":"Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst","author":"bansal","year":"2018","journal-title":"ar Xiv preprint"},{"key":"ref7","article-title":"Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction","author":"chai","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_32"},{"key":"ref4","article-title":"Scene transformer: A unified architecture for predicting future trajectories of multiple agents","author":"ngiam","year":"2021","journal-title":"International Conference on Learning Representations"},{"key":"ref3","article-title":"Multimodal trajectory predictions for autonomous driving using deep convolutional networks","author":"cui","year":"2018","journal-title":"ar Xiv preprint"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/IROS51168.2021.9636035"},{"key":"ref5","article-title":"Thomas: Trajectory heatmap output with learned multi-agent sam-pling","author":"gilles","year":"2021","journal-title":"ArXiv Preprint"},{"key":"ref40","article-title":"Argoverse 2: Next generation datasets for self-driving perception and forecasting","author":"wilson","year":"2021","journal-title":"Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)"}],"event":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","location":"London, United Kingdom","start":{"date-parts":[[2023,5,29]]},"end":{"date-parts":[[2023,6,2]]}},"container-title":["2023 IEEE International Conference on Robotics and Automation (ICRA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10160211\/10160212\/10160984.pdf?arnumber=10160984","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T17:36:22Z","timestamp":1690220182000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10160984\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,29]]},"references-count":41,"URL":"https:\/\/doi.org\/10.1109\/icra48891.2023.10160984","relation":{},"subject":[],"published":{"date-parts":[[2023,5,29]]}}}