{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T06:32:31Z","timestamp":1723271551191},"reference-count":27,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T00:00:00Z","timestamp":1622332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T00:00:00Z","timestamp":1622332800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T00:00:00Z","timestamp":1622332800000},"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":[[2021,5,30]]},"DOI":"10.1109\/icra48506.2021.9561536","type":"proceedings-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T00:28:35Z","timestamp":1634689715000},"source":"Crossref","is-referenced-by-count":3,"title":["Ellipse Loss for Scene-Compliant Motion Prediction"],"prefix":"10.1109","author":[{"given":"Henggang","family":"Cui","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hoda","family":"Shajari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sai","family":"Yalamanchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nemanja","family":"Djuric","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","article-title":"Trajectron++: Multi-agent generative trajectory forecasting with heterogeneous data for control","author":"salzmann","year":"2020"},{"key":"ref11","article-title":"Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction","author":"chai","year":"2019"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00240"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01240"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00144"},{"key":"ref15","first-page":"137","article-title":"Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks","author":"kosaraju","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8793868"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093332"},{"key":"ref18","article-title":"Predicting motion of vulnerable road users using high-definition maps and efficient convnets","author":"chou","year":"2020","journal-title":"IEEE Intelligent Vehicles Symposium (IV)"},{"key":"ref19","article-title":"Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst","author":"bansal","year":"2018"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403283"},{"key":"ref27","first-page":"8026","article-title":"Pytorch: An imperative style, high-performance deep learning library","author":"paszke","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref3","article-title":"Discrete residual flow for probabilistic pedestrian behavior prediction","author":"jain","year":"2019"},{"key":"ref6","article-title":"Multixnet: Multiclass multistage multimodal motion prediction","author":"djuric","year":"2020"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00376"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.233"},{"key":"ref7","article-title":"Long-term prediction of vehicle behavior using short-term uncertainty-aware trajectories and high-definition maps","author":"yalamanchi","year":"2020","journal-title":"IEEE International Conference on Intelligent Transportation Systems (ITSC)"},{"key":"ref2","article-title":"Scene compliant trajectory forecast with agent-centric spatio-temporal grids","author":"ridel","year":"2019"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00246"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2014.2306552"},{"key":"ref20","first-page":"947","article-title":"Intentnet: Learning to predict intention from raw sensor data","author":"casas","year":"2018","journal-title":"Conference on Robot Learning"},{"key":"ref22","article-title":"Spatially-aware graph neural networks for relational behavior forecasting from sensor data","author":"casas","year":"2019"},{"key":"ref21","first-page":"8660","article-title":"End-to-end interpretable neural motion planner","author":"zeng","year":"2019","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref24","article-title":"Laserflow: Efficient and probabilistic object detection and motion forecasting","author":"meyer","year":"2020"},{"key":"ref23","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR42600.2020.01140","article-title":"Motionnet: Joint perception and motion prediction for autonomous driving based on bird’s eye view maps","author":"wu","year":"2020"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01296"},{"key":"ref25","article-title":"Improving movement prediction of traffic actors using off-road loss and bias mitigation","author":"niedoba","year":"2019","journal-title":"Workshop on ’Machine Learning for Autonomous Driving’ at Conference on Neural Information Processing Systems"}],"event":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","location":"Xi'an, China","start":{"date-parts":[[2021,5,30]]},"end":{"date-parts":[[2021,6,5]]}},"container-title":["2021 IEEE International Conference on Robotics and Automation (ICRA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9560720\/9560666\/09561536.pdf?arnumber=9561536","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:47:13Z","timestamp":1652197633000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9561536\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,30]]},"references-count":27,"URL":"https:\/\/doi.org\/10.1109\/icra48506.2021.9561536","relation":{},"subject":[],"published":{"date-parts":[[2021,5,30]]}}}