{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T21:04:51Z","timestamp":1776891891052,"version":"3.51.2"},"reference-count":41,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.neucom.2026.133275","type":"journal-article","created":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T07:48:00Z","timestamp":1773474480000},"page":"133275","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Semi-supervised driving style recognition via deep metric learning and liquid time-constant networks"],"prefix":"10.1016","volume":"682","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8819-4484","authenticated-orcid":false,"given":"Shangwu","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Ruochen","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Renkai","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Qing","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Yingfeng","family":"Cai","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.133275_bib0005","article-title":"Driver behavior modeling: developments and future directions","volume":"2016","author":"AbuAli","year":"2016","journal-title":"Int. J. Veh. Technol."},{"key":"10.1016\/j.neucom.2026.133275_bib0010","series-title":"British Machine Vision Conference","first-page":"3","article-title":"Learning local feature descriptors with triplets and shallow convolutional neural networks","author":"Balntas","year":"2016"},{"key":"10.1016\/j.neucom.2026.133275_bib0015","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1109\/TITS.2019.2896672","article-title":"Convolutional neural network with adaptive regularization to classify driving styles on smartphones","volume":"21","author":"Bejani","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2026.133275_bib0020","series-title":"International Joint Conference on Neural Networks (IJCNN)","first-page":"3016","article-title":"Exploiting the use of recurrent neural networks for driver behavior profiling","author":"Carvalho","year":"2017"},{"key":"10.1016\/j.neucom.2026.133275_bib0025","series-title":"International Conference on Machine Learning Technologies (ICMLT)","first-page":"195","article-title":"Liquid DINO: a multi-task neural network towards autonomous driving","author":"Chatziloizos","year":"2025"},{"key":"10.1016\/j.neucom.2026.133275_bib0030","first-page":"1550","article-title":"Semi-traj2graph identifying fine-grained driving style with GPS trajectory data via multi-task learning","volume":"8","author":"Chen","year":"2021","journal-title":"IEEE Trans. Big Data"},{"key":"10.1016\/j.neucom.2026.133275_bib0035","doi-asserted-by":"crossref","first-page":"13976","DOI":"10.1109\/TITS.2023.3297986","article-title":"Driving style feature extraction and recognition based on hyperdimensional computing and semi-supervised twin projection vector machine","volume":"24","author":"Chen","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2026.133275_bib0040","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2022.1023470","article-title":"Extended liquid state machines for speech recognition","volume":"16","author":"Deckers","year":"2022","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.neucom.2026.133275_bib0045","first-page":"909","article-title":"Liquidt: stock market analysis using liquid time-constant neural networks","volume":"16","author":"Gajjar","year":"2024","journal-title":"Int. J. Inf. Technol."},{"key":"10.1016\/j.neucom.2026.133275_bib0050","series-title":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"6830","article-title":"T-EnFP: an efficient transformer encoder-based system for driving behavior classification","author":"Guo","year":"2024"},{"key":"10.1016\/j.neucom.2026.133275_bib0055","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"7657","article-title":"Liquid time-constant networks","author":"Hasani","year":"2021"},{"key":"10.1016\/j.neucom.2026.133275_bib0060","author":"Hermans"},{"key":"10.1016\/j.neucom.2026.133275_bib0065","series-title":"International Workshop on Similarity-Based Pattern Recognition","first-page":"84","article-title":"Deep metric learning using triplet network","author":"Hoffer","year":"2015"},{"key":"10.1016\/j.neucom.2026.133275_bib0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115041","article-title":"Cost-sensitive semi-supervised deep learning to assess driving risk by application of naturalistic vehicle trajectories","volume":"178","author":"Hu","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.133275_bib0075","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1109\/TITS.2015.2462084","article-title":"Driver behavior analysis for safe driving: a survey","volume":"16","author":"Kaplan","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2026.133275_bib0080","series-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"7255","article-title":"Embedding expansion: augmentation in embedding space for deep metric learning","author":"Ko","year":"2020"},{"key":"10.1016\/j.neucom.2026.133275_bib0085","series-title":"ICML Workshop on Challenges in Representation Learning","first-page":"896","article-title":"Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks","author":"Lee","year":"2013"},{"key":"10.1016\/j.neucom.2026.133275_bib0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2020.107589","article-title":"Extraction of descriptive driving patterns from driving data using unsupervised algorithms","volume":"156","author":"Li","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.neucom.2026.133275_bib0095","series-title":"IEEE 63rd Conference on Decision and Control (CDC)","first-page":"793","article-title":"Liquid-graph time-constant network for multi-agent systems control","author":"Marino","year":"2024"},{"key":"10.1016\/j.neucom.2026.133275_bib0100","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1109\/TITS.2017.2706978","article-title":"Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey","volume":"19","author":"Martinez","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2026.133275_bib0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2020.102917","article-title":"Classifying travelers\u2019 driving style using basic safety messages generated by connected vehicles: application of unsupervised machine learning","volume":"122","author":"Mohammadnazar","year":"2021","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"10.1016\/j.neucom.2026.133275_bib0110","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.3390\/electronics8101088","article-title":"Face recognition via deep learning using data augmentation based on orthogonal experiments","volume":"8","author":"Pei","year":"2019","journal-title":"Electronics"},{"key":"10.1016\/j.neucom.2026.133275_bib0115","series-title":"IEEE International Conference on Intelligent Transportation Systems (ITSC)","first-page":"387","article-title":"Need data for driver behaviour analysis? Presenting the public uah-driveset","author":"Romera","year":"2016"},{"key":"10.1016\/j.neucom.2026.133275_bib0120","series-title":"IEEE International Conference on Intelligent Transportation Systems (ITSC)","first-page":"1","article-title":"Driving behavior classification based on sensor data fusion using LSTM recurrent neural networks","author":"Saleh","year":"2017"},{"key":"10.1016\/j.neucom.2026.133275_bib0125","series-title":"IEEE Intelligent Vehicles Symposium (IV)","first-page":"602","article-title":"Multivariate time series analysis for driving style classification using neural networks and hyperdimensional computing","author":"Schlegel","year":"2021"},{"key":"10.1016\/j.neucom.2026.133275_bib0130","series-title":"IEEE Conference on Computer Vision and Pattern Recognition","first-page":"815","article-title":"Facenet: a unified embedding for face recognition and clustering","author":"Schroff","year":"2015"},{"key":"10.1016\/j.neucom.2026.133275_bib0135","article-title":"Improved deep metric learning with multi-class n-pair loss objective","volume":"29","author":"Sohn","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.133275_bib0140","first-page":"596","article-title":"Fixmatch: simplifying semi-supervised learning with consistency and confidence","volume":"33","author":"Sohn","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.133275_bib0145","series-title":"Journal of Physics: Conference Series","first-page":"012019","article-title":"Driving style recognition of leading vehicles based on semi-supervised Gaussian mixture model","author":"Song","year":"2023"},{"key":"10.1016\/j.neucom.2026.133275_bib0150","doi-asserted-by":"crossref","DOI":"10.3390\/agronomy14102290","article-title":"Deep learning-enabled dynamic model for nutrient status detection of aquaponically grown plants","volume":"14","author":"Taha","year":"2024","journal-title":"Agronomy"},{"key":"10.1016\/j.neucom.2026.133275_bib0155","article-title":"Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results","volume":"30","author":"Tarvainen","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.133275_bib0160","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1613\/jair.1.12664","article-title":"A theoretical perspective on hyperdimensional computing","volume":"72","author":"Thomas","year":"2021","journal-title":"J. Artif. Intell. Res."},{"key":"10.1016\/j.neucom.2026.133275_bib0165","series-title":"International Joint Conference on Neural Networks (IJCNN)","first-page":"1","article-title":"Transdbc: transformer for multivariate time-series based driver behavior classification","author":"Vyas","year":"2022"},{"key":"10.1016\/j.neucom.2026.133275_bib0170","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1093\/ietisy\/e89-d.3.1188","article-title":"Driver identification using driving behavior signals","volume":"89","author":"Wakita","year":"2006","journal-title":"IEICE Trans. Inf. Syst."},{"key":"10.1016\/j.neucom.2026.133275_bib0175","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1109\/THMS.2017.2736948","article-title":"Driving style classification using a semisupervised support vector machine","volume":"47","author":"Wang","year":"2017","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"10.1016\/j.neucom.2026.133275_bib0180","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btae452","article-title":"Hypergen: compact and efficient genome sketching using hyperdimensional vectors","volume":"40","author":"Xu","year":"2024","journal-title":"Bioinformatics"},{"key":"10.1016\/j.neucom.2026.133275_bib0185","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A review of recurrent neural networks: LSTM cells and network architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Comput."},{"key":"10.1016\/j.neucom.2026.133275_bib0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2020.104096","article-title":"Dynamic clustering analysis for driving styles identification","volume":"97","author":"de Zepeda","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.neucom.2026.133275_bib0195","first-page":"18408","article-title":"Flexmatch: boosting semi-supervised learning with curriculum pseudo labeling","volume":"34","author":"Zhang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.133275_bib0200","doi-asserted-by":"crossref","first-page":"4223","DOI":"10.1109\/TVT.2019.2903110","article-title":"Vehicle driving behavior recognition based on multi-view convolutional neural network with joint data augmentation","volume":"68","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"10.1016\/j.neucom.2026.133275_bib0205","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.3390\/agriculture12081083","article-title":"Identifying field crop diseases using transformer-embedded convolutional neural network","volume":"12","author":"Zhu","year":"2022","journal-title":"Agriculture"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226006727?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226006727?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:31:47Z","timestamp":1776889907000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226006727"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":41,"alternative-id":["S0925231226006727"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133275","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Semi-supervised driving style recognition via deep metric learning and liquid time-constant networks","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133275","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"133275"}}