{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:23:44Z","timestamp":1777569824845,"version":"3.51.4"},"reference-count":49,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,10,23]],"date-time":"2022-10-23T00:00:00Z","timestamp":1666483200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,23]],"date-time":"2022-10-23T00:00:00Z","timestamp":1666483200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["545934\u20132020"],"award-info":[{"award-number":["545934\u20132020"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,23]]},"DOI":"10.1109\/iros47612.2022.9982283","type":"proceedings-article","created":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T19:38:15Z","timestamp":1672083495000},"page":"12196-12203","source":"Crossref","is-referenced-by-count":23,"title":["Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty"],"prefix":"10.1109","author":[{"given":"Boris","family":"Ivanovic","sequence":"first","affiliation":[{"name":"NVIDIA Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuan-Hui","family":"Lee","sequence":"additional","affiliation":[{"name":"Toyota Research Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pavel","family":"Tokmakov","sequence":"additional","affiliation":[{"name":"Toyota Research Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Blake","family":"Wulfe","sequence":"additional","affiliation":[{"name":"Toyota Research Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rowan","family":"Mcllister","sequence":"additional","affiliation":[{"name":"Toyota Research Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adrien","family":"Gaidon","sequence":"additional","affiliation":[{"name":"Toyota Research Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Pavone","sequence":"additional","affiliation":[{"name":"NVIDIA Research"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364920917446"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/661"},{"key":"ref3","article-title":"Probabilistic object detection: Strengths, weaknesses, opportunities","volume-title":"Workshop on AI for Autonomous Driving at the International Conference on Machine Learning (ICML)","author":"Bhatt"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58523-5_40"},{"key":"ref5","article-title":"One thousand and one hours: Self-driving motion prediction dataset","volume-title":"Conf. on Robot Learning","author":"Houston"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-control-060117-105157"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1186\/s40648-014-0001-z"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33016120"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00731"},{"key":"ref10","article-title":"A variational approach to simultaneous tracking and classification of multiple objects","volume-title":"17th International Conference on Information Fusion (FUSION)","author":"Romero-Cano"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-018-1104-4"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_47"},{"key":"ref13","article-title":"Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction","volume-title":"Conference on Robot Learning (CoRL)","author":"Chai"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8460766"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2018.8594393"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00246"},{"key":"ref17","article-title":"Generative adversarial nets","volume-title":"Conf. on Neural Information Processing Systems","author":"Goodfellow"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00240"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2019.8916927"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00376"},{"key":"ref21","first-page":"947","article-title":"Intentnet: Learning to predict intention from raw sensor data","volume-title":"Conference on Robot Learning (CoRL)","author":"Casas"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9196697"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58592-1_37"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/IV48863.2021.9575718"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/72.809073"},{"key":"ref26","article-title":"Natural-parameter networks: A class of probabilistic neural networks","volume-title":"Conf. on Neural Information Processing Systems","author":"Wang"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2011-196"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00291"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref30","article-title":"Interaction networks for learning about objects, relations and physics","volume-title":"Conf. on Neural Information Processing Systems","author":"Battaglia"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.573"},{"key":"ref32","article-title":"Learning structured output representation using deep conditional generative models","volume-title":"Conf. on Neural Information Processing Systems","author":"Sohn"},{"key":"ref33","article-title":"Evidential sparsification of multimodal latent spaces in conditional variational autoencoders","volume-title":"Conf. on Neural Information Processing Systems","author":"Itkina"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/d14-1179"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1115\/1.3662552"},{"key":"ref36","first-page":"54","article-title":"The extended Kalman filter","volume-title":"Probabilistic Robotics","author":"Thrun","year":"2005"},{"key":"ref37","first-page":"743","article-title":"Better unicycle models","volume-title":"Planning Algorithms","author":"LaValle","year":"2006"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2016.2578706"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015885"},{"key":"ref40","doi-asserted-by":"crossref","DOI":"10.1109\/IROS47612.2022.9982283","volume-title":"Heterogeneous-agent trajectory forecasting incorporating class uncertainty","author":"Ivanovic","year":"2022"},{"key":"ref41","article-title":"On calibration of modern neural networks","volume-title":"Int. Conf. on Machine Learning","author":"Guo"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref44","article-title":"MATS: An interpretable trajectory forecasting representation for planning and control","volume-title":"Conf. on Robot Learning","author":"Ivanovic"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783313"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.2307\/j.ctvcm4g18.8"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/k16-1002"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2969925"},{"key":"ref49","article-title":"Categorial reparameterization with gumbel-softmax","volume-title":"Int. Conf. on Learning Representations","author":"Jang"}],"event":{"name":"2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","location":"Kyoto, Japan","start":{"date-parts":[[2022,10,23]]},"end":{"date-parts":[[2022,10,27]]}},"container-title":["2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9981026\/9981028\/09982283.pdf?arnumber=9982283","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T04:17:31Z","timestamp":1706761051000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9982283\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,23]]},"references-count":49,"URL":"https:\/\/doi.org\/10.1109\/iros47612.2022.9982283","relation":{},"subject":[],"published":{"date-parts":[[2022,10,23]]}}}