{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T09:47:27Z","timestamp":1723628847108},"reference-count":57,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"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":[[2021,6]]},"DOI":"10.1109\/cvpr46437.2021.00095","type":"proceedings-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T21:56:02Z","timestamp":1635890162000},"source":"Crossref","is-referenced-by-count":33,"title":["SceneGen: Learning to Generate Realistic Traffic Scenes"],"prefix":"10.1109","author":[{"given":"Shuhan","family":"Tan","sequence":"first","affiliation":[{"name":"Uber Advanced Technologies Group"}]},{"given":"Kelvin","family":"Wong","sequence":"additional","affiliation":[{"name":"Uber Advanced Technologies Group"}]},{"given":"Shenlong","family":"Wang","sequence":"additional","affiliation":[{"name":"Uber Advanced Technologies Group"}]},{"given":"Sivabalan","family":"Manivasagam","sequence":"additional","affiliation":[{"name":"Uber Advanced Technologies Group"}]},{"given":"Mengye","family":"Ren","sequence":"additional","affiliation":[{"name":"Uber Advanced Technologies Group"}]},{"given":"Raquel","family":"Urtasun","sequence":"additional","affiliation":[{"name":"Uber Advanced Technologies Group"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Language models are unsupervised multitask learners","author":"radford","year":"2018"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01240-3_33"},{"key":"ref33","article-title":"Polygen: An autoregressive generative model of 3d meshes","author":"nash","year":"2020","journal-title":"ICML"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01118"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2018.8569938"},{"key":"ref30","article-title":"On kinematic waves. II. A theory of traffic flow on long crowded roads","author":"lighthill","year":"1955","journal-title":"Royal Society of London"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794443"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1063\/1.1721265"},{"key":"ref35","article-title":"Traffic flow simulation using corsim","author":"owen","year":"2001","journal-title":"Winter Simulation Conference"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1287\/opre.9.2.209"},{"key":"ref28","article-title":"Learning lane graph representations for motion forecasting","author":"liang","year":"2020","journal-title":"ECCV"},{"key":"ref27","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"ICLRE"},{"key":"ref29","article-title":"Efficient graph generation with graph recurrent attention networks","author":"liao","year":"2019","journal-title":"NeurIPS"},{"key":"ref2","article-title":"Modeling high-dimensional discrete data with multi-layer neural networks","author":"bengio","year":"1999","journal-title":"NeurIPS"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.51.1035"},{"key":"ref20","article-title":"A neural representation of sketch drawings","author":"ha","year":"2018","journal-title":"ICLRE"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref21","article-title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","author":"heusel","year":"2017","journal-title":"NeurIPS"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2019.8813776"},{"key":"ref23","article-title":"The curious case of neural text degeneration","author":"holtzman","year":"2020","journal-title":"ICLRE"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00465"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00999"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201362"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2016.7795683"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.379"},{"key":"ref56","article-title":"Graphrnn: Generating realistic graphs with deep auto-regressive models","author":"you","year":"2018","journal-title":"ICML"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/S0968-090X(96)00006-X"},{"key":"ref54","article-title":"HDNET: exploiting HD maps for 3d object detection","author":"yang","year":"2018","journal-title":"CoRL"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58574-7_19"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2015.55"},{"key":"ref10","article-title":"Augmented lidar simulator for autonomous driving","author":"fang","year":"2019"},{"key":"ref11","article-title":"Vissim: A microscopic simulation tool to evaluate actuated signal control including bus priority","author":"fellendorf","year":"1994"},{"key":"ref40","doi-asserted-by":"crossref","DOI":"10.1287\/opre.4.1.42","article-title":"Shock waves on the highway","author":"richards","year":"1956","journal-title":"Operations Research"},{"key":"ref12","article-title":"Does the wake-sleep algorithm produce good density estimators?","author":"frey","year":"1995","journal-title":"NeurIPS"},{"key":"ref13","doi-asserted-by":"crossref","DOI":"10.1287\/opre.9.4.545","article-title":"Nonlinear follow-the-leader models of traffic flow","author":"gazis","year":"1961","journal-title":"Operations Research"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995641"},{"key":"ref15","article-title":"Joint 3d estimation of objects and scene layout","author":"geiger","year":"2011","journal-title":"NeurIPS"},{"key":"ref16","author":"gerlough","year":"1955","journal-title":"Simulation of Freeway Traffic on a General-Purpose Discrete Variable Computer"},{"key":"ref17","article-title":"Computer program multsim for simulating output from vehicle detectors on a multi-lane signal-controlled road","author":"gipps","year":"1976"},{"key":"ref18","article-title":"Generating sequences with recurrent neural networks","author":"graves","year":"2013","journal-title":"CoRR"},{"key":"ref19","article-title":"A kernel two-sample test","author":"gretton","year":"2012","journal-title":"JMLR"},{"key":"ref4","article-title":"Demystifying MMD gans","author":"binkowski","year":"2018","journal-title":"ICLRE"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.2307\/2346732"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1287\/opre.6.2.165"},{"key":"ref5","author":"bishop","year":"2006","journal-title":"Pattern Recognition and Machine Learning"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58520-4_42"},{"key":"ref7","article-title":"Argo-verse: 3d tracking and forecasting with rich maps","author":"chang","year":"2019","journal-title":"CVPR"},{"key":"ref49","article-title":"Pixel recurrent neural networks","author":"van den oord","year":"2016","journal-title":"ICML"},{"key":"ref9","article-title":"CARLA: an open urban driving simulator","author":"dosovitskiy","year":"2017","journal-title":"CoRL"},{"key":"ref46","article-title":"RNADE: the real-valued neural autoregressive density-estimator","author":"uria","year":"2013","journal-title":"NeurIPS"},{"key":"ref45","article-title":"NADE: the real-valued neural autoregressive density-estimator","author":"uria","year":"2013","journal-title":"CoRR"},{"key":"ref48","article-title":"Wavenet: A generative model for raw audio","author":"van den oord","year":"2016","journal-title":"ISCA"},{"key":"ref47","article-title":"A deep and tractable density estimator","author":"uria","year":"2014","journal-title":"ICML"},{"key":"ref42","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","author":"shi","year":"2015","journal-title":"NeurIPS"},{"key":"ref41","article-title":"Improved techniques for training gans","author":"salimans","year":"2016","journal-title":"NeurIPS"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.62.1805"},{"key":"ref43","author":"taheri","year":"1990","journal-title":"An investigation and design of slip control braking systems integrated with four wheel steering"}],"event":{"name":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","location":"Nashville, TN, USA","start":{"date-parts":[[2021,6,20]]},"end":{"date-parts":[[2021,6,25]]}},"container-title":["2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9577055\/9577056\/09577588.pdf?arnumber=9577588","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T23:18:31Z","timestamp":1659482311000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9577588\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6]]},"references-count":57,"URL":"https:\/\/doi.org\/10.1109\/cvpr46437.2021.00095","relation":{},"subject":[],"published":{"date-parts":[[2021,6]]}}}