{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T20:10:20Z","timestamp":1736971820637,"version":"3.33.0"},"reference-count":40,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T00:00:00Z","timestamp":1728864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T00:00:00Z","timestamp":1728864000000},"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":[[2024,10,14]]},"DOI":"10.1109\/iros58592.2024.10802492","type":"proceedings-article","created":{"date-parts":[[2024,12,25]],"date-time":"2024-12-25T19:17:39Z","timestamp":1735154259000},"page":"5910-5917","source":"Crossref","is-referenced-by-count":0,"title":["Pre-training on Synthetic Driving Data for Trajectory Prediction"],"prefix":"10.1109","author":[{"given":"Yiheng","family":"Li","sequence":"first","affiliation":[{"name":"University of California,Berkeley"}]},{"given":"Seth Z.","family":"Zhao","sequence":"additional","affiliation":[{"name":"University of California,Berkeley"}]},{"given":"Chenfeng","family":"Xu","sequence":"additional","affiliation":[{"name":"University of California,Berkeley"}]},{"given":"Chen","family":"Tang","sequence":"additional","affiliation":[{"name":"University of California,Berkeley"}]},{"given":"Chenran","family":"Li","sequence":"additional","affiliation":[{"name":"University of California,Berkeley"}]},{"given":"Mingyu","family":"Ding","sequence":"additional","affiliation":[{"name":"University of California,Berkeley"}]},{"given":"Masayoshi","family":"Tomizuka","sequence":"additional","affiliation":[{"name":"University of California,Berkeley"}]},{"given":"Wei","family":"Zhan","sequence":"additional","affiliation":[{"name":"University of California,Berkeley"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00957"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19842-7_3"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00895"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01661"},{"key":"ref6","article-title":"Iterative imitation policy improvement for interactive autonomous driving","volume":"abs\/2109.01288","author":"Yin","year":"2021"},{"article-title":"Pre-training with synthetic data helps offline reinforcement learning","year":"2024","author":"Wang","key":"ref7"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.512"},{"journal-title":"Is synthetic data from generative models ready for image recognition","year":"2023","author":"He","key":"ref9"},{"article-title":"What matters to you? towards visual representation alignment for robot learning","volume-title":"The Twelfth International Conference on Learning Representations","author":"Tian","key":"ref10"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00902"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00059"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00502"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01216-8_12"},{"key":"ref16","first-page":"5085","article-title":"Transfer learning via learning to transfer","volume-title":"Proceedings of the 35th International Conference on Machine Learning","volume":"80","author":"Wei"},{"issue":"12","key":"ref17","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"Journal of machine learning research"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01499"},{"key":"ref19","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"International conference on machine learning","author":"Chen"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref22","first-page":"2","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","volume-title":"Proceedings of naacL-HLT","volume":"1","author":"Devlin"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00647"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.763"},{"article-title":"Pix2seq: A language modeling framework for object detection","volume-title":"International Conference on Learning Representations 2022","author":"Chen","key":"ref25"},{"key":"ref26","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proceedings of the 38th International Conference on Machine Learning","volume":"139","author":"Radford"},{"article-title":"Meta-transformer: A unified framework for multimodal learning","year":"2023","author":"Zhang","key":"ref27"},{"issue":"2017","key":"ref28","first-page":"1","article-title":"The effectiveness of data augmentation in image classification using deep learning","volume":"11","author":"Wang","year":"2017","journal-title":"Convolutional Neural Networks Vis. Recognit"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.15607\/rss.2018.xiv.019"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00669"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00294"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00331"},{"article-title":"Exploiting map information for self-supervised learning in motion forecasting","year":"2022","author":"Azevedo","key":"ref33"},{"key":"ref34","first-page":"1793","article-title":"Ssl-lanes: Self-supervised learning for motion forecasting in autonomous driving","volume-title":"Proceedings of The 6th Conference on Robot Learning","volume":"205","author":"Bhattacharyya"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00767"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00797"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01154"},{"key":"ref38","first-page":"35632","article-title":"Mcmae: Masked convolution meets masked autoencoders","volume-title":"Advances in Neural Information Processing Systems","volume":"35","author":"Gao","year":"2022"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01502"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/IV55156.2024.10588510"}],"event":{"name":"2024 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","start":{"date-parts":[[2024,10,14]]},"location":"Abu Dhabi, United Arab Emirates","end":{"date-parts":[[2024,10,18]]}},"container-title":["2024 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10801246\/10801290\/10802492.pdf?arnumber=10802492","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T19:29:49Z","timestamp":1736969389000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10802492\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,14]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/iros58592.2024.10802492","relation":{},"subject":[],"published":{"date-parts":[[2024,10,14]]}}}