{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T18:02:58Z","timestamp":1770228178898,"version":"3.49.0"},"reference-count":79,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/00048\/2020"],"award-info":[{"award-number":["UIDB\/00048\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Transport. Syst."],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1109\/tits.2023.3268578","type":"journal-article","created":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T18:52:57Z","timestamp":1683139977000},"page":"9253-9267","source":"Crossref","is-referenced-by-count":2,"title":["Probabilistic Approach for Road-Users Detection"],"prefix":"10.1109","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8988-0205","authenticated-orcid":false,"given":"Gledson","family":"Melotti","sequence":"first","affiliation":[{"name":"Federal Institute of Espírito Santo, Electrical Technical Coordination, São Mateus, Espírito Santo, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4766-9962","authenticated-orcid":false,"given":"Weihao","family":"Lu","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, University of Glasgow, Glasgow, U.K"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0280-6705","authenticated-orcid":false,"given":"Pedro","family":"Conde","sequence":"additional","affiliation":[{"name":"Institute of Systems and Robotics (ISR), University of Coimbra, Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9848-372X","authenticated-orcid":false,"given":"Dezong","family":"Zhao","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, University of Glasgow, Glasgow, U.K"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9645-5346","authenticated-orcid":false,"given":"Alireza","family":"Asvadi","sequence":"additional","affiliation":[{"name":"IADYS, Roquefort-la-Bédoule, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1854-049X","authenticated-orcid":false,"given":"Nuno","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Institute of Systems and Robotics (ISR), University of Coimbra, Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2168-2077","authenticated-orcid":false,"given":"Cristiano","family":"Premebida","sequence":"additional","affiliation":[{"name":"Institute of Systems and Robotics (ISR), University of Coimbra, Coimbra, Portugal"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"ref57","first-page":"1","article-title":"Addressing failure prediction by learning model confidence","volume":"32","author":"corbi\u00e8re","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-31865-1_25"},{"key":"ref56","first-page":"180","article-title":"Relaxed softmax: Efficient confidence auto-calibration for safe pedestrian detection","author":"neumann","year":"2018","journal-title":"Proc NIPS Workshop Machine Learn Intell Transp Syst"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00018"},{"key":"ref14","first-page":"1","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref58","first-page":"1","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","author":"hendrycks","year":"2017","journal-title":"Proc 5th Int Conf Learn Represent"},{"key":"ref53","first-page":"6402","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume":"30","author":"lakshminarayanan","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref52","first-page":"190","article-title":"Flipout: Efficient pseudo-independent weight perturbations on mini-batches","author":"wen","year":"2018","journal-title":"Proc 6th Int Conf Learn Represent (ICLR)"},{"key":"ref11","first-page":"1147","author":"zhang","year":"2009","journal-title":"F Measure"},{"key":"ref55","first-page":"1","article-title":"Concrete dropout","volume":"30","author":"gal","year":"2017","journal-title":"Proc 31st Adv Neural Inf Process Syst"},{"key":"ref10","article-title":"YOLOv4: Optimal speed and accuracy of object detection","author":"bochkovskiy","year":"2020","journal-title":"arXiv 2004 10934"},{"key":"ref54","first-page":"6448","article-title":"Does label smoothing mitigate label noise?","volume":"119","author":"lukasik","year":"2020","journal-title":"Proc 37th Int Conf Mach Learn"},{"key":"ref17","first-page":"1","article-title":"An image is worth 16×16 words: Transformers for image recognition at scale","author":"dosovitskiy","year":"2021","journal-title":"Proc 9th Int Conf Learn Represent"},{"key":"ref16","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume":"97","author":"tan","year":"2019","journal-title":"Proc 36th Int Conf Mach Learn"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/661"},{"key":"ref18","article-title":"MLP-mixer: An all-MLP architecture for vision","author":"tolstikhin","year":"2021","journal-title":"arXiv 2105 01601"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2018.8569814"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-44938-8_7"},{"key":"ref46","first-page":"782","article-title":"Adaptive-TTA: Accuracy-consistent weighted test time augmentation method for the uncertainty calibration of deep learning classifiers","author":"conde","year":"2022","journal-title":"Proc 33rd Brit Mach Vis Conf"},{"key":"ref45","author":"bishop","year":"2006","journal-title":"Pattern Recognition and Machine Learning"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.2172\/1525811"},{"key":"ref47","article-title":"Being bayesian, even just a bit, fixes overconfidence in ReLU networks","author":"kristiadi","year":"2020","journal-title":"arXiv 2002 10118"},{"key":"ref42","first-page":"1453","article-title":"Regularization via structural label smoothing","volume":"108","author":"li","year":"2020","journal-title":"Proc 23rd Int Conf Artif Intell Statist"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"61","DOI":"10.7551\/mitpress\/1113.003.0008","article-title":"Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods","author":"platt","year":"2000","journal-title":"Proc Adv Large Margin Classifiers"},{"key":"ref44","first-page":"5574","article-title":"What uncertainties do we need in Bayesian deep learning for computer vision?","volume":"30","author":"kendall","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref43","first-page":"1","article-title":"When does label smoothing help?","volume":"32","author":"m\u00fcller","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.7148\/2015-0488"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cvi.2013.0257"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1177\/0278364916679498"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01258-8_39"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1561\/9781680836899"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3215766"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2913998"},{"key":"ref5","first-page":"1","article-title":"Creating autonomous vehicle systems","volume":"6","author":"liu","year":"2017","journal-title":"Synth Lectures Comput Sci"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/775107.775151"},{"key":"ref35","first-page":"1613","article-title":"Weight uncertainty in neural network","volume":"37","author":"blundell","year":"2015","journal-title":"Proc ICML"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102430"},{"key":"ref34","first-page":"1890","article-title":"Variational dropout and the local reparameterization trick","volume":"28","author":"kingma","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/ICARSC49921.2020.9096138"},{"key":"ref37","first-page":"2348","article-title":"Practical variational inference for neural networks","volume":"24","author":"graves","year":"2011","journal-title":"Proc 24th Int Conf Neural Inf Process"},{"key":"ref36","first-page":"1","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2014","journal-title":"Proc 2nd Int Conf Learn Represent (ICLR)"},{"key":"ref31","first-page":"1321","article-title":"On calibration of modern neural networks","volume":"70","author":"guo","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref75","first-page":"345","article-title":"SGDR: Stochastic gradient descent with warm restarts","author":"loshchilov","year":"2017","journal-title":"Proc 5th Int Conf Learn Represent"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00608"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"ref33","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","volume":"48","author":"gal","year":"2016","journal-title":"Proc 33rd Int Conf Mach Learn (PMLR)"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.3390\/s18103337"},{"key":"ref32","article-title":"Regularizing neural networks by penalizing confident output distributions","author":"pereyra","year":"2017","journal-title":"arXiv 1701 06548"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01215"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"3292","DOI":"10.1109\/TPAMI.2022.3179507","article-title":"KITTI-360: A novel dataset and benchmarks for urban scene understanding in 2D and 3D","volume":"45","author":"liao","year":"2023","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3150906"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.23919\/EUSIPCO55093.2022.9909866"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01334"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1145\/2934732.2934737"},{"key":"ref70","author":"papoulis","year":"2001","journal-title":"Probability random variables and stochastic processes"},{"key":"ref73","article-title":"Note on the general case of the bayes-laplace formula for inductive or a posteriori probabilities","volume":"8","author":"lidstone","year":"1920","journal-title":"Trans Faculty Actuaries"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1006\/csla.1999.0128"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00159"},{"key":"ref68","first-page":"2498","article-title":"Variational dropout sparsifies deep neural networks","volume":"70","author":"molchanov","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn (PMLR)"},{"key":"ref23","first-page":"1","article-title":"Labels are not perfect: Improving probabilistic object detection via label uncertainty","author":"feng","year":"2020","journal-title":"Proc Eur Conf Comput Vis Workshop Perception Auto Driving"},{"key":"ref67","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2952000"},{"key":"ref25","first-page":"18237","article-title":"Improving model calibration with accuracy versus uncertainty optimization","volume":"33","author":"krishnan","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref69","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3096854"},{"key":"ref64","article-title":"Learning confidence for out-of-distribution detection in neural networks","author":"devries","year":"2018","journal-title":"arXiv 1802 04865"},{"key":"ref63","first-page":"24","article-title":"Intriguing properties of neural networks","author":"szegedy","year":"2014","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref22","first-page":"1","article-title":"Labels are not perfect: Inferring spatial uncertainty in object detection","volume":"8","author":"feng","year":"2021","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref66","article-title":"Uncertainty in deep learning","author":"gal","year":"2016"},{"key":"ref21","first-page":"1","article-title":"Probabilistic object classification using CNN ML-MAP layers","author":"melotti","year":"2020","journal-title":"Proc Eur Conf Comput Vis Workshop Perception Auto Driving"},{"key":"ref65","first-page":"10","article-title":"Enhancing the reliability of out-of-distribution image detection in neural networks","author":"liang","year":"2018","journal-title":"Proc 6th Int Conf Learn Represent"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2980004"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2995884"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2019.8814046"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3153723"},{"key":"ref62","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"2014","journal-title":"arXiv 1412 6572"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00917"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6979\/10235283\/10115057.pdf?arnumber=10115057","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T18:51:28Z","timestamp":1729363888000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10115057\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":79,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tits.2023.3268578","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9]]}}}