{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T01:14:01Z","timestamp":1779153241159,"version":"3.51.4"},"reference-count":44,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,9,19]],"date-time":"2021-09-19T00:00:00Z","timestamp":1632009600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,9,19]],"date-time":"2021-09-19T00:00:00Z","timestamp":1632009600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,19]],"date-time":"2021-09-19T00:00:00Z","timestamp":1632009600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001711","name":"Swiss National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,19]]},"DOI":"10.1109\/itsc48978.2021.9564545","type":"proceedings-article","created":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T19:52:26Z","timestamp":1635191546000},"page":"2938-2943","source":"Crossref","is-referenced-by-count":42,"title":["Out-of-Distribution Detection for Automotive Perception"],"prefix":"10.1109","author":[{"given":"Julia","family":"Nitsch","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masha","family":"Itkina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ransalu","family":"Senanayake","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Nieto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Max","family":"Schmidt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roland","family":"Siegwart","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mykel J.","family":"Kochenderfer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cesar","family":"Cadena","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref38","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"0","journal-title":"International Conference on Artificial Intelligence and Statistics (AISTATS)"},{"key":"ref33","first-page":"4077","article-title":"Prototypical networks for few-shot learning","author":"snell","year":"0","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref32","first-page":"9758","article-title":"Deep anomaly detection using geometric transformations","author":"golan","year":"0","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00088"},{"key":"ref30","author":"zenati","year":"2018","journal-title":"Efficient GAN-Based Anomaly Detection"},{"key":"ref37","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"0","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref36","article-title":"Training confidence-calibrated classifiers for detecting out-of-distribution samples","author":"lee","year":"0","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref35","author":"papadopoulos","year":"2019","journal-title":"Outlier exposure with confidence control for out-of-distribution detection"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1364"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.2307\/2289995"},{"key":"ref11","first-page":"13991","article-title":"Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift","author":"ovadia","year":"0","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s42354-019-0238-z"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2974682"},{"key":"ref14","article-title":"Deep anomaly detection with outlier exposure","author":"hendrycks","year":"0","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref15","author":"devries","year":"2018","journal-title":"Learning confidence for out-of-distribution detection in neural networks"},{"key":"ref16","first-page":"2672","author":"goodfellow","year":"2014","journal-title":"Generative adversarial nets"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref18","author":"caesar","year":"2019","journal-title":"nuscenes A multimodal dataset for autonomous driving"},{"key":"ref19","author":"jospin","year":"2020","journal-title":"Hands-on Bayesian neural networks–a tutorial for deep learning users"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00356"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58595-2_35"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6015"},{"key":"ref3","author":"zhu","year":"2019","journal-title":"Class-balanced grouping and sampling for point cloud 3d object detection"},{"key":"ref6","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","author":"hendrycks","year":"0","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref29","author":"donahue","year":"2016","journal-title":"Adversarial feature learning"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1177\/0278364918770733"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TASL.2011.2141988"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1182"},{"key":"ref2","author":"bhattacharyya","year":"2020","journal-title":"Deformable PV-RCNN Improving 3D object detection with learned deformations"},{"key":"ref9","first-page":"445","article-title":"The case against accuracy estimation for comparing induction algorithms","author":"foster","year":"0","journal-title":"International Conference on Machine Learning (ICML)"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00102"},{"key":"ref20","author":"wehenkel","year":"2020","journal-title":"You say normalizing flows I see Bayesian networks"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2967313"},{"key":"ref21","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"0","journal-title":"International Conference on Machine Learning (ICML)"},{"key":"ref42","article-title":"Learning common and transferable feature representations for multimodal data","author":"nitsch","year":"0","journal-title":"IEEE Intelligent Vehicles Symposium (IV)"},{"key":"ref24","first-page":"7167","article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","author":"lee","year":"0","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1999.10485670"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2967706"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00294"},{"key":"ref26","author":"lee","year":"2020","journal-title":"Detect reject correct Crossmodal compensation of corrupted sensors"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref25","first-page":"1","article-title":"Variational autoencoder based anomaly detection using reconstruction probability","volume":"2","author":"an","year":"2015","journal-title":"Special Lecture on IE"}],"event":{"name":"2021 IEEE International Intelligent Transportation Systems Conference (ITSC)","location":"Indianapolis, IN, USA","start":{"date-parts":[[2021,9,19]]},"end":{"date-parts":[[2021,9,22]]}},"container-title":["2021 IEEE International Intelligent Transportation Systems Conference (ITSC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9564393\/9564395\/09564545.pdf?arnumber=9564545","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T16:51:05Z","timestamp":1652201465000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9564545\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,19]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/itsc48978.2021.9564545","relation":{},"subject":[],"published":{"date-parts":[[2021,9,19]]}}}