{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T22:00:44Z","timestamp":1769724044427,"version":"3.49.0"},"reference-count":49,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"NSF","award":["2312487"],"award-info":[{"award-number":["2312487"]}]},{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"publisher","award":["FA9550-21-1-0164"],"award-info":[{"award-number":["FA9550-21-1-0164"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Robot. Autom. Lett."],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1109\/lra.2024.3405810","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T17:24:00Z","timestamp":1716830640000},"page":"6480-6487","source":"Crossref","is-referenced-by-count":1,"title":["Automated Generation of Transformations to Mitigate Sensor Hardware Migration in ADS"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4641-4199","authenticated-orcid":false,"given":"Meriel","family":"von Stein","sequence":"first","affiliation":[{"name":"School of Engineering, Computer Science Department, University of Virginia, Charlottesville, VA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6524-9195","authenticated-orcid":false,"given":"Hongning","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Engineering, Computer Science Department, University of Virginia, Charlottesville, VA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9592-1352","authenticated-orcid":false,"given":"Sebastian","family":"Elbaum","sequence":"additional","affiliation":[{"name":"School of Engineering, Computer Science Department, University of Virginia, Charlottesville, VA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3043505"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/QRS-C51114.2020.00048"},{"key":"ref3","article-title":"The Waymo drivers training regimen: How structured testing prepares our self-driving technology for the real world","year":"2020"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2016.7841045"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/IROS47612.2022.9982116"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01839"},{"key":"ref7","article-title":"Waymo open dataset","year":"2022"},{"key":"ref8","article-title":"Camera calibration with OpenCV","year":"2022"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00895"},{"key":"ref10","article-title":"Argoverse 2: Next generation datasets for self-driving perception and forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wilson","year":"2021"},{"key":"ref11","article-title":"Tesla AP1 vs AP2 vs AP3difference between autopilot versions","year":"2022"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2975643"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3194085.3194087"},{"key":"ref14","article-title":"A comprehensive review of past and present image inpainting methods","volume-title":"Comput. Vis. Image Understanding","volume":"203","author":"Jam","year":"2021"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"ref16","article-title":"How transferable are features in deep neural networks?","volume":"27","author":"Yosinski","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2016.2583491"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2019.2896470"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2832296"},{"key":"ref20","first-page":"607","article-title":"Emergence of simple-cell receptive field properties by learning a sparse code for natural images","volume-title":"Nature","volume":"381","author":"Olshausen","year":"1996"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899x\/1116\/1\/012118"},{"key":"ref22","article-title":"A comprehensive survey on design and application of autoencoder in deep learning","volume-title":"Appl. Soft Comput.","volume":"138","author":"Li","year":"2023"},{"key":"ref23","article-title":"Autoencoders","author":"Bank","year":"2020"},{"key":"ref24","first-page":"6309","article-title":"Neural discrete representation learning","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Oord","year":"2017"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-11009-3_31"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00389"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00182"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00698"},{"key":"ref29","first-page":"3884","article-title":"On warm-starting neural network training","volume-title":"Adv. Neural Inf. Process. Syst.","volume":"33","author":"Ash","year":"2020"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299173"},{"key":"ref31","article-title":"Revisiting small batch training for deep neural networks","author":"Masters","year":"2018"},{"key":"ref32","first-page":"88","article-title":"Cost-sensitive learning with neural networks","volume-title":"Proc. Eur. Conf. Artif. Intell.","author":"Kukar","year":"1998"},{"key":"ref33","first-page":"29915","article-title":"Optimizing data collection for machine learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Mahmood","year":"2022"},{"key":"ref34","article-title":"Countering adversarial images using input transformations","author":"Guo","year":"2017"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00034"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.12376"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33266-1_36"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3417063"},{"key":"ref39","article-title":"Diagnosing and enhancing VAE models","author":"Dai","year":"2019"},{"key":"ref40","article-title":"Beamng. Drive vehicle simulator","year":"2020"},{"key":"ref41","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR42600.2020.00252","article-title":"Scalability in perception for autonomous driving: Waymo open dataset","volume-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","author":"Sun","year":"2020"},{"key":"ref42","article-title":"End to end learning for self-driving cars","author":"Bojarski","year":"2016"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-019-01630-9"},{"issue":"25","key":"ref44","doi-asserted-by":"crossref","first-page":"32859","DOI":"10.1364\/OE.23.032859","article-title":"Radial lens distortion correction with sub-pixel accuracy for X-Ray micro-tomography","volume":"23","author":"Vo","year":"2015","journal-title":"Opt. Exp."},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00207"},{"key":"ref46","article-title":"Pillow image library","year":"2023"},{"key":"ref47","first-page":"1950","article-title":"Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning","volume-title":"Proc. 36th Int. Conf. Neural Inf. Process. Syst.","author":"Liu","year":"2024"},{"key":"ref48","article-title":"Quantizing deep convolutional networks for efficient inference: A whitepaper","author":"Krishnamoorthi","year":"2018"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1145\/3551349.3559500"}],"container-title":["IEEE Robotics and Automation Letters"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/7083369\/10534628\/10539233-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7083369\/10534628\/10539233.pdf?arnumber=10539233","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T21:01:06Z","timestamp":1719349266000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10539233\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7]]},"references-count":49,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/lra.2024.3405810","relation":{},"ISSN":["2377-3766","2377-3774"],"issn-type":[{"value":"2377-3766","type":"electronic"},{"value":"2377-3774","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7]]}}}