{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T14:23:57Z","timestamp":1771511037897,"version":"3.50.1"},"reference-count":51,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,6,5]],"date-time":"2022-06-05T00:00:00Z","timestamp":1654387200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,6,5]],"date-time":"2022-06-05T00:00:00Z","timestamp":1654387200000},"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":[[2022,6,5]]},"DOI":"10.1109\/iv51971.2022.9827083","type":"proceedings-article","created":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T15:33:28Z","timestamp":1658244808000},"page":"419-426","source":"Crossref","is-referenced-by-count":14,"title":["Assessing Cross-dataset Generalization of Pedestrian Crossing Predictors"],"prefix":"10.1109","author":[{"given":"Joseph","family":"Gesnouin","sequence":"first","affiliation":[{"name":"Institut VEDECOM,Versailles,France,78000"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steve","family":"Pechberti","sequence":"additional","affiliation":[{"name":"Institut VEDECOM,Versailles,France,78000"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bogdan","family":"Stanciulescu","sequence":"additional","affiliation":[{"name":"Universit&#x00E9; PSL,Centre de Robotique, MINES ParisTech,Paris,France,75006"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabien","family":"Moutarde","sequence":"additional","affiliation":[{"name":"Universit&#x00E9; PSL,Centre de Robotique, MINES ParisTech,Paris,France,75006"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","author":"hendrycks","year":"2016","journal-title":"arXiv preprint arXiv 1610 02984"},{"key":"ref38","first-page":"13 991","article-title":"Can you trust your model&#x2019;s uncertainty? evaluating predictive uncertainty under dataset shift","volume":"32","author":"ovadia","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.329"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01117"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVCI54083.2021.9661140"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00260"},{"key":"ref37","first-page":"917","article-title":"Uncertainty-aware attention for reliable interpretation and prediction","author":"heo","year":"2018","journal-title":"Proceedings of the 32Nd International Conference on Neural Information Processing Systems"},{"key":"ref36","first-page":"1321","article-title":"On calibration of modern neural networks","author":"guo","year":"2017","journal-title":"International Conference on Machine Learning"},{"key":"ref35","article-title":"Obtaining well calibrated probabilities using bayesian binning","author":"naeini","year":"2015","journal-title":"Twenty-Ninth AAAI Conference on Artificial Intelligence"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics10010044"},{"key":"ref28","article-title":"Intformer: Predicting pedestrian intention with the aid of the transformer architecture","author":"lorenzo","year":"2021","journal-title":"arXiv preprint arXiv 2105 08647"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3390\/s21175694"},{"key":"ref29","author":"bapu sridhar","year":"2020","journal-title":"Pedestrian intent prediction using deep machine learning"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00130"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ISEEE48094.2019.9136126"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2019.8917118"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2018.8500657"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00441"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/IV47402.2020.9304591"},{"key":"ref26","article-title":"Predicting pedestrian crossing intention with feature fusion and spatiotemporal attention","author":"yang","year":"2021","journal-title":"arXiv preprint arXiv 2104 05485"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299101"},{"key":"ref50","article-title":"Flipout: Efficient pseudo-independent weight perturbations on mini-batches","author":"wen","year":"2018","journal-title":"arXiv preprint arXiv 1803 04386"},{"key":"ref51","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume":"30","author":"lakshminarayanan","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref10","first-page":"802","article-title":"Convolutional lstm network: A machine learning approach for precipitation nowcasting","volume":"2015","author":"shi","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794278"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00010"},{"key":"ref12","first-page":"4489","article-title":"Learning spatiotemporal features with 3d convolutional networks","author":"tran","year":"2015","journal-title":"Proceedings of the IEEE International Conference on Computer Vision"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.502"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093426"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8793991"},{"key":"ref16","article-title":"Is attention to bounding boxes all you need for pedestrian action prediction?","author":"achaji","year":"2021","journal-title":"arXiv preprint arXiv 2107 08031"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.2352\/ISSN.2470-1173.2020.16.AVM-109"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3390\/a13120331"},{"key":"ref19","first-page":"1","article-title":"Trouspinet: Spatio-temporal attention on parallel atrous convolutions and ugrus for skeletal pedestrian crossing prediction","author":"gesnouin","year":"2021","journal-title":"2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.12125"},{"key":"ref3","article-title":"Are we done with imagenet?","author":"beyer","year":"2020","journal-title":"arXiv preprint arXiv 2006 04989"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2017.33"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01231-1_31"},{"key":"ref8","first-page":"arxiv:1409.1556","article-title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","author":"simonyan","year":"2014","journal-title":"ArXiv e-prints"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/SITIS.2018.00109"},{"key":"ref49","first-page":"1050","article-title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"2016","journal-title":"International Conference on Machine Learning"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref46","article-title":"Asymmetrical bi-rnn for pedestrian trajectory encoding","author":"rozenberg","year":"2021","journal-title":"arXiv preprint arXiv 2106 01111"},{"key":"ref45","first-page":"7291","article-title":"Realtime multi-person 2d pose estimation using part affinity fields","author":"cao","year":"2017","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref48","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"chung","year":"2014","journal-title":"arXiv preprint arXiv 1412 3555"},{"key":"ref47","article-title":"Pedestrian action anticipation using contextual feature fusion in stacked rnns","author":"rasouli","year":"2019","journal-title":"BMVC"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00636"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2017.33"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.223"},{"key":"ref43","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"dem\u0161ar","year":"2006","journal-title":"The Journal of Machine Learning Research"}],"event":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","location":"Aachen, Germany","start":{"date-parts":[[2022,6,4]]},"end":{"date-parts":[[2022,6,9]]}},"container-title":["2022 IEEE Intelligent Vehicles Symposium (IV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9826996\/9826997\/09827083.pdf?arnumber=9827083","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T16:04:15Z","timestamp":1659974655000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9827083\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,5]]},"references-count":51,"URL":"https:\/\/doi.org\/10.1109\/iv51971.2022.9827083","relation":{},"subject":[],"published":{"date-parts":[[2022,6,5]]}}}