{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T08:06:41Z","timestamp":1773389201069,"version":"3.50.1"},"reference-count":99,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"vor","delay-in-days":13,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100015321","name":"Universidade de Tr\u00e1s-os-Montes e Alto Douro","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100015321","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The automotive sector is undergoing continuous technological evolution driven by the demand for sustainable and safe vehicles. Among the main factors influencing safety, driver behaviour has been identified as a critical contributor to road crashes. This systematic review explores recent innovations in detecting risky driver behaviours, addressing six research questions: the most relevant datasets used for algorithm development and evaluation; system architectures and methodologies for anomaly detection; the most studied driver behaviours and related environmental, human, and mechanical factors; advances in machine learning, deep learning, and statistical methods; performance metrics and validation approaches; and the role of embedded technologies and sensors in practical applications. The review included 93 peer-reviewed articles published between 2020 and 2024, sourced from ACM, IEEE, ScienceDirect, and Scopus. Exclusion criteria were duplicates, non-open access, retracted works, and studies unrelated to outlier detection or driver behaviour. The Parsifal tool was used to support systematic data processing. Results highlight the most frequently used datasets, proposed models, and their performance in detecting driver behaviours, as well as the influence of contextual factors such as traffic rules, road conditions, and sensor limitations. Despite advances, real-world integration remains challenging, requiring further research and development. This review aims to guide researchers in understanding the current state of anomaly detection in driving contexts and to emphasize the need for broader collaboration to create effective, deployable solutions that enhance road safety worldwide.<\/jats:p>","DOI":"10.1007\/s10462-026-11492-y","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T10:37:21Z","timestamp":1769683041000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advances on risky driver behaviour detection in road vehicles: a systematic literature review"],"prefix":"10.1007","volume":"59","author":[{"given":"Lu\u00eds","family":"Ferreira","sequence":"first","affiliation":[]},{"given":"Ant\u00f3nio","family":"Valente","sequence":"additional","affiliation":[]},{"given":"Paulo","family":"Salgado","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9","family":"Boaventura","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"issue":"4","key":"11492_CR1","doi-asserted-by":"publisher","first-page":"3336","DOI":"10.1109\/TITS.2020.3035700","volume":"23","author":"AE Abdelrahman","year":"2022","unstructured":"Abdelrahman AE et al (2022) Robust data-driven framework for driver behavior profiling using supervised machine learning. IEEE Trans Intell Transp Syst 23(4):3336\u20133350. https:\/\/doi.org\/10.1109\/TITS.2020.3035700","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"11492_CR2","doi-asserted-by":"publisher","first-page":"93075","DOI":"10.1109\/ACCESS.2024.3423723","volume":"12","author":"J Alguindigue","year":"2024","unstructured":"Alguindigue J et al (2024) Biosignals monitoring for driver drowsiness detection using deep neural networks. IEEE Access 12:93075\u201393086. https:\/\/doi.org\/10.1109\/ACCESS.2024.3423723","journal-title":"IEEE Access"},{"key":"11492_CR3","doi-asserted-by":"publisher","first-page":"79403","DOI":"10.1109\/ACCESS.2022.3185251","volume":"10","author":"R Alharbey","year":"2022","unstructured":"Alharbey R et al (2022) Fatigue state detection for tired persons in presence of driving periods. IEEE Access 10:79403\u201379418. https:\/\/doi.org\/10.1109\/ACCESS.2022.3185251","journal-title":"IEEE Access"},{"key":"11492_CR4","doi-asserted-by":"publisher","unstructured":"Alqahtani H, Kumar G (2021) Machine learning for enhancing transportation security: a comprehensive analysis of electric and flying vehicle systems. In: Engineering applications of artificial intelligence. pp 173\u2013196. https:\/\/doi.org\/10.1016\/j.engappai.2023.107667","DOI":"10.1016\/j.engappai.2023.107667"},{"key":"11492_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122590","author":"C Axenie","year":"2024","unstructured":"Axenie C et al (2024) Fuzzy modelling and inference for physics-aware road vehicle driver behaviour model calibration. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2023.122590","journal-title":"Expert Syst Appl"},{"issue":"6","key":"11492_CR6","doi-asserted-by":"publisher","first-page":"5271","DOI":"10.1109\/TITS.2023.3330159","volume":"25","author":"S Banerjee","year":"2024","unstructured":"Banerjee S et al (2024) A blockchain-enabled sustainable safety management framework for connected vehicles. IEEE Trans Intell Transp Syst 25(6):5271\u20135281. https:\/\/doi.org\/10.1109\/TITS.2023.3330159","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"11492_CR7","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1016\/j.jksuci.2019.08.003","volume":"33","author":"FZ Benjelloun","year":"2021","unstructured":"Benjelloun FZ et al (2021) Improving outliers detection in data streams using LiCS and voting. Journal of King Saud University - Computer and Information Sciences 33:1177\u20131185. https:\/\/doi.org\/10.1016\/j.jksuci.2019.08.003","journal-title":"Journal of King Saud University - Computer and Information Sciences"},{"key":"11492_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2024.114196","volume":"180","author":"M Boersma","year":"2024","unstructured":"Boersma M et al (2024) Outlier detection using flexible categorization and interrogative agendas. Decis Support Syst 180:114196. https:\/\/doi.org\/10.1016\/j.dss.2024.114196","journal-title":"Decis Support Syst"},{"key":"11492_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3436541","author":"H Cao","year":"2024","unstructured":"Cao H (2024) The detection of abnormal behavior by artificial intelligence algorithms under network security. IEEE Access Pract Innov Open Solut. https:\/\/doi.org\/10.1109\/ACCESS.2024.3436541","journal-title":"IEEE Access Pract Innov Open Solut"},{"issue":"12","key":"11492_CR10","doi-asserted-by":"publisher","first-page":"20120","DOI":"10.1109\/TITS.2024.3446832","volume":"25","author":"J Chen","year":"2024","unstructured":"Chen J et al (2024) A driving risk assessment framework considering driver\u2019s fatigue state and distraction behavior. IEEE Trans Intell Transp Syst 25(12):20120\u201320136. https:\/\/doi.org\/10.1109\/TITS.2024.3446832","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"11492_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2023.100510","volume":"14","author":"TJ Chengula","year":"2023","unstructured":"Chengula TJ, Mwakalonge J, Comert G, Siuhi S (2023) Improving road safety with ensemble learning: detecting driver anomalies using vehicle inbuilt cameras. Mach Learn Appl 14:100510. https:\/\/doi.org\/10.1016\/j.mlwa.2023.100510","journal-title":"Mach Learn Appl"},{"issue":"5","key":"11492_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2024.100580","volume":"17","author":"TJ Chengula","year":"2024","unstructured":"Chengula TJ, Mwakalonge J, Comert G, Sulle M et al (2024) Enhancing advanced driver assistance systems through explainable artificial intelligence for driver anomaly detection. Mach Learn Appl 17(5):100580. https:\/\/doi.org\/10.1016\/j.mlwa.2024.100580","journal-title":"Mach Learn Appl"},{"key":"11492_CR13","doi-asserted-by":"publisher","unstructured":"Choi YA et al (2021) Unsupervised driver behavior profiling leveraging recurrent neural networks. In: Information security applications: 22nd international conference, pp 28\u201338. https:\/\/doi.org\/10.1007\/978-3-030-89432-0_3","DOI":"10.1007\/978-3-030-89432-0_3"},{"key":"11492_CR14","doi-asserted-by":"publisher","unstructured":"Comuni F et al (2022) Passive and active learning of driver behavior from electric vehicles. In: 2022 IEEE 25th international conference on intelligent transportation systems (ITSC), pp 929\u2013936. https:\/\/doi.org\/10.1109\/ITSC55140.2022.9922012","DOI":"10.1109\/ITSC55140.2022.9922012"},{"key":"11492_CR15","doi-asserted-by":"publisher","first-page":"160347","DOI":"10.1109\/ACCESS.2021.3131402","volume":"9","author":"A Degirmenci","year":"2023","unstructured":"Degirmenci A, Karal O (2023) Robust incremental outlier detection approach based on a new metric in data streams. IEEE Access Pract Innov Open Solut 9:160347\u2013160360. https:\/\/doi.org\/10.1109\/ACCESS.2021.3131402","journal-title":"IEEE Access Pract Innov Open Solut"},{"key":"11492_CR16","doi-asserted-by":"publisher","unstructured":"Ding Y et al (2022) EgoSpeed-net: forecasting speed-control in driver behavior from egocentric video data. In: SIGSPATIAL \u201922, pp 105\u2013117. https:\/\/doi.org\/10.1145\/3557915.3560946","DOI":"10.1145\/3557915.3560946"},{"key":"11492_CR17","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.jclinepi.2020.07.005","volume":"127","author":"TF Frandsen","year":"2020","unstructured":"Frandsen TF et al (2020) Using the full PICO model as a search tool for systematic reviews resulted in lower recall for some PICO elements. J Clin Epidemiol 127:69\u201375. https:\/\/doi.org\/10.1016\/j.jclinepi.2020.07.005","journal-title":"J Clin Epidemiol"},{"key":"11492_CR18","doi-asserted-by":"publisher","first-page":"26624","DOI":"10.1109\/ACCESS.2023.3257238","volume":"11","author":"B Fu","year":"2023","unstructured":"Fu B et al (2023) Catastrophic causes of truck drivers\u2019 crashes on Brazilian highways: mixed method analyses and crash prediction using machine learning. IEEE Access Pract Innov Open Solut 11:26624\u201326636. https:\/\/doi.org\/10.1109\/ACCESS.2023.3257238","journal-title":"IEEE Access Pract Innov Open Solut"},{"key":"11492_CR19","doi-asserted-by":"publisher","unstructured":"Gao X et al (2024a) Driver behavior analysis in diverging area based on driving simulation. In: ICBAR \u201923, pp 574\u2013579. https:\/\/doi.org\/10.1145\/3656766.3656863","DOI":"10.1145\/3656766.3656863"},{"issue":"10","key":"11492_CR20","doi-asserted-by":"publisher","first-page":"14115","DOI":"10.1109\/TITS.2024.3396640","volume":"25","author":"H Gao","year":"2024","unstructured":"Gao H et al (2024b) Learning driver-irrelevant features for generalizable driver behavior recognition. IEEE Trans Intell Transp Syst 25(10):14115\u201314127. https:\/\/doi.org\/10.1109\/TITS.2024.3396640","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"11492_CR21","doi-asserted-by":"publisher","unstructured":"Girija M, Divya V (2024) Road traffic accident prediction using deep learning. pp 148\u2013159. https:\/\/doi.org\/10.1109\/ICC-ROBINS60238.2024.10533897","DOI":"10.1109\/ICC-ROBINS60238.2024.10533897"},{"key":"11492_CR22","doi-asserted-by":"publisher","unstructured":"Goncu S et al (2022) Analysis on effects of driving behavior on freeway traffic flow: a comparative evaluation of two driver profiles using two car-following models. pp 688\u2013693. https:\/\/doi.org\/10.1109\/IV51971.2022.9827296","DOI":"10.1109\/IV51971.2022.9827296"},{"issue":"9","key":"11492_CR23","doi-asserted-by":"publisher","first-page":"11602","DOI":"10.1109\/TITS.2024.3381175","volume":"25","author":"MZ Hasan","year":"2024","unstructured":"Hasan MZ et al (2024) Vision-language models can identify distracted driver behavior from naturalistic videos. IEEE Trans Intell Transp Syst 25(9):11602\u201311616. https:\/\/doi.org\/10.1109\/TITS.2024.3381175","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"11492_CR24","doi-asserted-by":"publisher","unstructured":"Hayashi H et al (2024) Elevating driver behavior understanding with RKnD: a novel probabilistic feature engineering approach. In: IEEE transactions on intelligent transportation systems. https:\/\/doi.org\/10.1109\/TITS.2024.3484452","DOI":"10.1109\/TITS.2024.3484452"},{"issue":"14","key":"11492_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/buildings14030835","volume":"835","author":"W He","year":"2024","unstructured":"He W, Chen M (2024) Advancing urban life: a systematic review of emerging technologies and artificial intelligence in urban design and planning. Buildings 835(14):1\u201321. https:\/\/doi.org\/10.3390\/buildings14030835","journal-title":"Buildings"},{"key":"11492_CR26","doi-asserted-by":"publisher","first-page":"2116","DOI":"10.1016\/j.procs.2023.10.202","volume":"225","author":"C Hory\u0144","year":"2023","unstructured":"Hory\u0144 C, Brzezi\u0144ska AN (2023) Detecting outliers in rule-based knowledge bases using self-organizing map and local outlier factor algorithms. Procedia Comput Sci 225:2116\u20132125. https:\/\/doi.org\/10.1016\/j.procs.2023.10.202","journal-title":"Procedia Comput Sci"},{"issue":"7","key":"11492_CR27","doi-asserted-by":"publisher","first-page":"8396","DOI":"10.1109\/TITS.2021.3080322","volume":"23","author":"J Huang","year":"2022","unstructured":"Huang J et al (2022) Driver glance behavior modeling based on semi-supervised clustering and piecewise aggregate representation. IEEE Trans Intell Transp Syst 23(7):8396\u20138411. https:\/\/doi.org\/10.1109\/TITS.2021.3080322","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"3","key":"11492_CR28","first-page":"1053","volume":"126","author":"Y Huo","year":"2020","unstructured":"Huo Y et al (2020) Traffic anomaly detection method based on improved GRU and EFMS-Kmeans clustering. Computer Model Eng Sci 126(3):1053\u20131091","journal-title":"Computer Model Eng Sci"},{"key":"11492_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2024.100530","volume":"15","author":"DA Indah","year":"2024","unstructured":"Indah DA et al (2024) Enhancing data efficiency for autonomous vehicles: using data sketches for detecting driving anomalies. Machine Learning with Applications 15:100530. https:\/\/doi.org\/10.1016\/j.mlwa.2024.100530","journal-title":"Machine Learning with Applications"},{"key":"11492_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2023.103820","volume":"237","author":"E Kamenou","year":"2023","unstructured":"Kamenou E et al (2023) LOFReg: an outlier-based regulariser for deep metric learning. Comput Vis Image Underst 237:103820. https:\/\/doi.org\/10.1016\/j.cviu.2023.103820","journal-title":"Comput Vis Image Underst"},{"issue":"5","key":"11492_CR31","doi-asserted-by":"publisher","first-page":"555","DOI":"10.3103\/S0146411624700664","volume":"58","author":"H Kang","year":"2024","unstructured":"Kang H et al (2024) Advancing driver behavior recognition: an intelligent approach utilizing ResNet. Auto Control Comput Sci 58(5):555\u2013568. https:\/\/doi.org\/10.3103\/S0146411624700664","journal-title":"Auto Control Comput Sci"},{"issue":"11","key":"11492_CR32","doi-asserted-by":"publisher","first-page":"11308","DOI":"10.1109\/JSEN.2023.3256000","volume":"23","author":"K Kanwal","year":"2023","unstructured":"Kanwal K et al (2023) Smartphone inertial measurement unit data features for analyzing driver driving behavior. IEEE Sens J 23(11):11308\u201311323. https:\/\/doi.org\/10.1109\/JSEN.2023.3256000","journal-title":"IEEE Sens J"},{"key":"11492_CR33","doi-asserted-by":"publisher","first-page":"129645","DOI":"10.1109\/ACCESS.2020.3009226","volume":"8","author":"NS Karuppusamy","year":"2020","unstructured":"Karuppusamy NS, Kang BY (2020) Multimodal system to detect driver fatigue using EEG, gyroscope, and image processing. IEEE Access 8:129645\u2013129667. https:\/\/doi.org\/10.1109\/ACCESS.2020.3009226","journal-title":"IEEE Access"},{"key":"11492_CR34","doi-asserted-by":"publisher","DOI":"10.3390\/s24144739","author":"D Kim","year":"2024","unstructured":"Kim D, Kim H et al (2024) Design and implementation of a two-wheeled vehicle safe driving evaluation system. Sensors. https:\/\/doi.org\/10.3390\/s24144739","journal-title":"Sensors"},{"key":"11492_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110846","volume":"156","author":"D Kim","year":"2024","unstructured":"Kim D, Park J et al (2024) Unsupervised outlier detection using random subspace and subsampling ensembles of Dirichlet process mixtures. Pattern Recogn 156:110846. https:\/\/doi.org\/10.1016\/j.patcog.2024.110846","journal-title":"Pattern Recogn"},{"key":"11492_CR36","doi-asserted-by":"publisher","first-page":"24431","DOI":"10.1109\/ACCESS.2023.3252004","volume":"11","author":"RR Koko","year":"2023","unstructured":"Koko RR et al (2023) Dynamic construction of outlier detector ensembles with bisecting K-means clustering. IEEE Access 11:24431\u201324447. https:\/\/doi.org\/10.1109\/ACCESS.2023.3252004","journal-title":"IEEE Access"},{"key":"11492_CR37","doi-asserted-by":"publisher","unstructured":"Lashkov I, Kashevnik A (2021) Aggressive behavior detection based on driver heart rate and hand movement data. In: 2021 IEEE international intelligent transportation systems conference (ITSC), pp 1490\u20131495. https:\/\/doi.org\/10.1109\/ITSC48978.2021.9564478","DOI":"10.1109\/ITSC48978.2021.9564478"},{"key":"11492_CR38","doi-asserted-by":"publisher","first-page":"83138","DOI":"10.1109\/ACCESS.2022.3197146","volume":"10","author":"T Li","year":"2022","unstructured":"Li T et al (2022a) AB-DLM: an improved deep learning model based on attention mechanism and BiFPN for driver distraction behavior detection. IEEE Access 10:83138\u201383151. https:\/\/doi.org\/10.1109\/ACCESS.2022.3197146","journal-title":"IEEE Access"},{"key":"11492_CR39","doi-asserted-by":"publisher","first-page":"30899","DOI":"10.1109\/ACCESS.2022.3159550","volume":"10","author":"H Li","year":"2022","unstructured":"Li H et al (2022b) An abnormal traffic detection model combined BiIndRNN with global attention. IEEE Access 10:30899\u201330912. https:\/\/doi.org\/10.1109\/ACCESS.2022.3159550","journal-title":"IEEE Access"},{"key":"11492_CR40","doi-asserted-by":"publisher","first-page":"47881","DOI":"10.1109\/ACCESS.2022.3171247","volume":"10","author":"DC Li","year":"2022","unstructured":"Li DC et al (2022c) Macroscopic big data analysis and prediction of driving behavior with an adaptive fuzzy recurrent neural network on the internet of vehicles. IEEE Access 10:47881\u201347895. https:\/\/doi.org\/10.1109\/ACCESS.2022.3171247","journal-title":"IEEE Access"},{"key":"11492_CR41","doi-asserted-by":"publisher","unstructured":"Li X et al (2024a) Research on characteristics of hesitant driving behavior in urban expressway diversion areas. pp 168\u2013175. https:\/\/doi.org\/10.1109\/IoTAAI62601.2024.10692627","DOI":"10.1109\/IoTAAI62601.2024.10692627"},{"issue":"3","key":"11492_CR42","doi-asserted-by":"publisher","first-page":"4488","DOI":"10.1109\/TCSS.2024.3350199","volume":"11","author":"R Li","year":"2024","unstructured":"Li R et al (2024b) Driving behavior prediction based on combined neural network mode. IEEE Trans Comput Soc Syst 11(3):4488\u20134496. https:\/\/doi.org\/10.1109\/TCSS.2024.3350199","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"11492_CR43","doi-asserted-by":"publisher","DOI":"10.1007\/s40430-024-04861-7","author":"C Liu","year":"2024","unstructured":"Liu C et al (2024a) An R-A dual network detection model for abnormal behavior of running vehicles. J Brazilian Soc Mech Sci Eng. https:\/\/doi.org\/10.1007\/s40430-024-04861-7","journal-title":"J Brazilian Soc Mech Sci Eng"},{"issue":"5","key":"11492_CR44","doi-asserted-by":"publisher","first-page":"6745","DOI":"10.1109\/TCSS.2024.3411486","volume":"11","author":"W Liu","year":"2024","unstructured":"Liu W et al (2024b) FMDNet: feature-attention-embedding-based multimodal-fusion driving-behavior-classification network. IEEE Trans Comput Soc Syst 11(5):6745\u20136758. https:\/\/doi.org\/10.1109\/TCSS.2024.3411486","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"11492_CR45","doi-asserted-by":"publisher","first-page":"21921","DOI":"10.1109\/ACCESS.2021.3055551","volume":"9","author":"Y Lu","year":"2021","unstructured":"Lu Y et al (2021) XGBoost algorithm-based monitoring model for urban driving stress: combining driving behaviour, driving environment, and route familiarity. IEEE Access 9:21921\u201321938. https:\/\/doi.org\/10.1109\/ACCESS.2021.3055551","journal-title":"IEEE Access"},{"key":"11492_CR46","doi-asserted-by":"publisher","first-page":"4198","DOI":"10.1109\/tmm.2020.3038311","volume":"23","author":"K Lv","year":"2021","unstructured":"Lv K et al (2021) Improving driver gaze prediction with reinforced attention. IEEE Trans Multimed 23:4198\u20134207. https:\/\/doi.org\/10.1109\/tmm.2020.3038311","journal-title":"IEEE Trans Multimed"},{"key":"11492_CR47","doi-asserted-by":"publisher","unstructured":"Lyu K et al (2020) Extract the gaze multi-dimensional information analysis driver behavior. In: ICMI \u201920, pp 790\u2013797. https:\/\/doi.org\/10.1145\/3382507.3417972","DOI":"10.1145\/3382507.3417972"},{"key":"11492_CR48","doi-asserted-by":"publisher","unstructured":"Ma N et al (2024a) Robust data-driven framework for driver behavior profiling using supervised machine learning. In: IEEE transactions on intelligent transportation systems. https:\/\/doi.org\/10.1109\/TITS.2024.3510538","DOI":"10.1109\/TITS.2024.3510538"},{"key":"11492_CR49","doi-asserted-by":"publisher","first-page":"37983","DOI":"10.1109\/access.2024.3374726","volume":"12","author":"B Ma","year":"2024","unstructured":"Ma B et al (2024b) Distracted driving behavior and driver\u2019s emotion detection based on improved YOLOv8 with attention mechanism. IEEE Access 12:37983\u201337994. https:\/\/doi.org\/10.1109\/access.2024.3374726","journal-title":"IEEE Access"},{"key":"11492_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107618","author":"AA Mohammed","year":"2024","unstructured":"Mohammed AA et al (2024) Driver distraction detection using semi-supervised lightweight vision transformer. Eng Appl Artif Intell. https:\/\/doi.org\/10.1016\/j.engappai.2023.107618","journal-title":"Eng Appl Artif Intell"},{"key":"11492_CR51","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.isatra.2024.06.007","volume":"152","author":"L Mu","year":"2024","unstructured":"Mu L et al (2024) A fault isolation strategy for industrial processes using outlier-degree-based variable contributions. ISA Trans 152:113\u2013128. https:\/\/doi.org\/10.1016\/j.isatra.2024.06.007","journal-title":"ISA Trans"},{"issue":"7","key":"11492_CR52","doi-asserted-by":"publisher","first-page":"377","DOI":"10.14569\/IJACSA.2024.0150737","volume":"15","author":"AR Muslikh","year":"2024","unstructured":"Muslikh AR et al (2024) Ensemble IDO method for outlier detection and N2O emission prediction in agriculture. Int J Adv Comput Sci Appl 15(7):377\u2013386. https:\/\/doi.org\/10.14569\/IJACSA.2024.0150737","journal-title":"Int J Adv Comput Sci Appl"},{"key":"11492_CR53","doi-asserted-by":"publisher","first-page":"71435","DOI":"10.1109\/access.2023.3293110","volume":"11","author":"A Muthuswamy","year":"2023","unstructured":"Muthuswamy A et al (2023) Driver distraction classification using deep convolutional autoencoder and ensemble learning. IEEE Access 11:71435\u201371448. https:\/\/doi.org\/10.1109\/access.2023.3293110","journal-title":"IEEE Access"},{"key":"11492_CR54","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.jpdc.2022.01.010","volume":"162.C","author":"M Nasr Azadani","year":"2022","unstructured":"Nasr Azadani M, Boukerche A (2022) DriverRep: Driver identification through driving behavior embeddings. J Parallel Distrib Comput 162.C:105\u2013117. https:\/\/doi.org\/10.1016\/j.jpdc.2022.01.010","journal-title":"J Parallel Distrib Comput"},{"key":"11492_CR55","doi-asserted-by":"publisher","first-page":"1420","DOI":"10.1016\/j.procs.2020.09.152","volume":"176","author":"A Nowak-Brzezi\u0144ska","year":"2020","unstructured":"Nowak-Brzezi\u0144ska A, Hory\u0144 C (2020) Outliers in rules - the comparision of LOF, COF and KMEANS algorithms. Procedia Comput Sci 176:1420\u20131429. https:\/\/doi.org\/10.1016\/j.procs.2020.09.152","journal-title":"Procedia Comput Sci"},{"key":"11492_CR56","doi-asserted-by":"publisher","first-page":"1420","DOI":"10.1016\/j.procs.2020.09.152","volume":"176","author":"A Nowak-Brzezi\u0144ska","year":"2022","unstructured":"Nowak-Brzezi\u0144ska A, Hory\u0144 C (2022) Outliers in rules - the comparision of LOF, COF and KMEANS algorithms. Procedia Comput Sci 176:1420\u20131429. https:\/\/doi.org\/10.1016\/j.procs.2020.09.152","journal-title":"Procedia Comput Sci"},{"key":"11492_CR57","doi-asserted-by":"publisher","unstructured":"Nowshin F et al (2024) Enhancing driving behavior analysis in autonomous systems: a reservoir computing and temporal-aware machine learning approach. pp 24\u201333. https:\/\/doi.org\/10.1109\/MOST60774.2024.00011","DOI":"10.1109\/MOST60774.2024.00011"},{"key":"11492_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107400","volume":"231","author":"B Ouyang","year":"2021","unstructured":"Ouyang B et al (2021) EBOD: an ensemble-based outlier detection algorithm for noisy datasets. Knowl Based Syst 231:107400. https:\/\/doi.org\/10.1016\/j.knosys.2021.107400","journal-title":"Knowl Based Syst"},{"key":"11492_CR59","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n71","author":"MJ Page","year":"2021","unstructured":"Page MJ et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. https:\/\/doi.org\/10.1136\/bmj.n71","journal-title":"BMJ"},{"key":"11492_CR60","doi-asserted-by":"publisher","unstructured":"Pakdamanian E et al (2021) DeepTake: prediction of driver takeover behavior using multimodal data. In: Proceedings of the 2021 CHI conference on human factors in computing systems. https:\/\/doi.org\/10.1145\/3411764.3445563","DOI":"10.1145\/3411764.3445563"},{"key":"11492_CR61","doi-asserted-by":"publisher","unstructured":"Park KH et al (2021) Stay as you were!: unsupervised driver behavior profiling through discovering normality on smartphone sensor measurements. pp 278\u2013284. https:\/\/doi.org\/10.1109\/ITSC48978.2021.9564814","DOI":"10.1109\/ITSC48978.2021.9564814"},{"key":"11492_CR62","doi-asserted-by":"publisher","first-page":"107250","DOI":"10.1109\/ACCESS.2021.3100980","volume":"9","author":"Y Peng","year":"2021","unstructured":"Peng Y et al (2021) Electricity theft detection in AMI based on clustering and local outlier factor. IEEE Access 9:107250\u2013107259. https:\/\/doi.org\/10.1109\/ACCESS.2021.3100980","journal-title":"IEEE Access"},{"key":"11492_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2023.113245","author":"L P\u00e9rez-Sala","year":"2023","unstructured":"P\u00e9rez-Sala L et al (2023) Deep learning model of convolutional neural networks powered by a genetic algorithm for prevention of traffic accidents severity. Chaos, Solitons & Fractals. https:\/\/doi.org\/10.1016\/j.chaos.2023.113245","journal-title":"Chaos, Solitons & Fractals"},{"key":"11492_CR64","doi-asserted-by":"publisher","unstructured":"Reyes Jr FC et al (2023) Analyzing truck driver\u2019s behavior on the road using YOLO v4 tiny algorithm. In: Proceedings of the 2022 6th international conference on computer science and artificial intelligence, pp 177\u2013183. https:\/\/doi.org\/10.1145\/3577530.3577559","DOI":"10.1145\/3577530.3577559"},{"key":"11492_CR65","doi-asserted-by":"publisher","DOI":"10.1186\/s12544-024-00655-z","author":"S Roussou","year":"2024","unstructured":"Roussou S et al (2024) Unfolding the dynamics of driving behavior: a machine learning analysis from Germany and Belgium. Eur Transp Res Rev. https:\/\/doi.org\/10.1186\/s12544-024-00655-z","journal-title":"Eur Transp Res Rev"},{"key":"11492_CR66","doi-asserted-by":"publisher","unstructured":"Sackmann M et al (2022) Modeling driver behavior using adversarial inverse reinforcement learning. In: 2022 IEEE intelligent vehicles symposium, pp 1683\u20131690. https:\/\/doi.org\/10.1109\/IV51971.2022.9827292","DOI":"10.1109\/IV51971.2022.9827292"},{"key":"11492_CR67","doi-asserted-by":"publisher","first-page":"12491","DOI":"10.1109\/ACCESS.2020.2963960","volume":"8","author":"BK Sava\u015f","year":"2020","unstructured":"Sava\u015f BK, Becerikli Y (2020) Real time driver fatigue detection system based on multi-task ConNN. IEEE Access Pract Innov Open Solut 8:12491\u201312498. https:\/\/doi.org\/10.1109\/ACCESS.2020.2963960","journal-title":"IEEE Access Pract Innov Open Solut"},{"issue":"16","key":"11492_CR68","doi-asserted-by":"publisher","first-page":"14053","DOI":"10.1007\/s00521-022-07141-4","volume":"34","author":"B Sava\u015f","year":"2022","unstructured":"Sava\u015f B, Becerikli Y (2022) Behavior-based driver fatigue detection system with deep belief network. Neural Comput Appl 34(16):14053\u201314065. https:\/\/doi.org\/10.1007\/s00521-022-07141-4","journal-title":"Neural Comput Appl"},{"key":"11492_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121347","volume":"237","author":"MA Sewwandi","year":"2024","unstructured":"Sewwandi MA et al (2024) k-outlier removal based on contextual label information and cluster purity for continuous data classification. Expert Syst Appl 237:121347. https:\/\/doi.org\/10.1016\/j.eswa.2023.121347","journal-title":"Expert Syst Appl"},{"key":"11492_CR70","doi-asserted-by":"publisher","first-page":"2037","DOI":"10.1109\/TNSRE.2023.3267114","volume":"31","author":"M Shahbakhti","year":"2023","unstructured":"Shahbakhti M et al (2023) Fusion of EEG and eye blink analysis for detection of driver fatigue. IEEE Trans Neural Syst Rehabil Eng 31:2037\u20132046. https:\/\/doi.org\/10.1109\/TNSRE.2023.3267114","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"11492_CR71","doi-asserted-by":"publisher","first-page":"65780","DOI":"10.1109\/ACCESS.2024.3397725","volume":"12","author":"M Shariful Islam","year":"2024","unstructured":"Shariful Islam M et al (2024) Elevating driver behavior understanding with RKnD: a novel probabilistic feature engineering approach. IEEE Access Pract Innov Open Solut 12:65780\u201365798. https:\/\/doi.org\/10.1109\/ACCESS.2024.3397725","journal-title":"IEEE Access Pract Innov Open Solut"},{"key":"11492_CR72","doi-asserted-by":"publisher","DOI":"10.1016\/j.multra.2024.100173","author":"RD Soliani","year":"2024","unstructured":"Soliani RD et al (2024) Catastrophic causes of truck drivers\u2019 crashes on Brazilian highways: mixed method analyses and crash prediction using machine learning. Multimodal Transp. https:\/\/doi.org\/10.1016\/j.multra.2024.100173","journal-title":"Multimodal Transp"},{"key":"11492_CR73","doi-asserted-by":"publisher","unstructured":"Song Q et al (2021) Towards efficient personalized driver behavior modeling with machine unlearning. In: CPS-IoT Week \u201923, pp 31\u201336. https:\/\/doi.org\/10.1145\/3576914.3587489","DOI":"10.1145\/3576914.3587489"},{"key":"11492_CR74","doi-asserted-by":"publisher","first-page":"70946","DOI":"10.1109\/ACCESS.2024.3394218","volume":"12","author":"XX Tang","year":"2024","unstructured":"Tang XX, Guo PY (2024) Fatigue driving detection methods based on drivers wearing sunglasses. IEEE Access Pract Innov Open Solut 12:70946\u201370962. https:\/\/doi.org\/10.1109\/ACCESS.2024.3394218","journal-title":"IEEE Access Pract Innov Open Solut"},{"key":"11492_CR75","doi-asserted-by":"publisher","first-page":"42834","DOI":"10.1109\/ACCESS.2024.3359756","volume":"12","author":"P Tao","year":"2024","unstructured":"Tao P et al (2024) Lane-changing decision intention prediction of surrounding drivers for intelligent driving. IEEE Access Pract Innov Open Solut 12:42834\u201342848. https:\/\/doi.org\/10.1109\/ACCESS.2024.3359756","journal-title":"IEEE Access Pract Innov Open Solut"},{"issue":"2","key":"11492_CR76","doi-asserted-by":"publisher","first-page":"1270","DOI":"10.1109\/TKDE.2021.3103571","volume":"35","author":"MB Toller","year":"2023","unstructured":"Toller MB, Geiger BC, Kern R (2023) Cluster purging: efficient outlier detection based on rate-distortion theory. IEEE Trans Knowl Data Eng 35(2):1270\u20131282. https:\/\/doi.org\/10.1109\/TKDE.2021.3103571","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"11492_CR77","doi-asserted-by":"publisher","unstructured":"Tolstaya E et al (2021) Identifying driver interactions via conditional behavior prediction. In: 2021 IEEE international conference on robotics and automation, pp 3473\u20133490. https:\/\/doi.org\/10.1109\/ICRA48506.2021.9561967","DOI":"10.1109\/ICRA48506.2021.9561967"},{"key":"11492_CR78","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.11780","volume":"206","author":"V Uher","year":"2022","unstructured":"Uher V et al (2022) Automation of cleaning and ensembles for outliers detection in questionnaire data. Expert Syst Appl 206:117809. https:\/\/doi.org\/10.1016\/j.eswa.2022.11780","journal-title":"Expert Syst Appl"},{"key":"11492_CR79","doi-asserted-by":"publisher","unstructured":"Wang H (2020) Residual mask based on MobileNet-V2 for driver\u2019s dangerous behavior recognition. In: CSAI \u201919, pp 196\u2013199. https:\/\/doi.org\/10.1145\/3374587.3374621","DOI":"10.1145\/3374587.3374621"},{"key":"11492_CR80","doi-asserted-by":"publisher","DOI":"10.1186\/s13638-020-1639-2","author":"H Wang","year":"2020","unstructured":"Wang H et al (2020a) A driver\u2019s car-following behavior prediction model based on multi-sensors data. EURASIP J Wirel Commun Netw. https:\/\/doi.org\/10.1186\/s13638-020-1639-2","journal-title":"EURASIP J Wirel Commun Netw"},{"key":"11492_CR81","doi-asserted-by":"publisher","first-page":"180422","DOI":"10.1109\/ACCESS.2020.3027811","volume":"8","author":"S Wang","year":"2020","unstructured":"Wang S et al (2020) Identification of driver braking intention based on long short-term memory (LSTM) network. IEEE Access 8:180422\u2013180432. https:\/\/doi.org\/10.1109\/ACCESS.2020.3027811","journal-title":"IEEE Access"},{"issue":"2","key":"11492_CR82","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.eij.2020.06.001","volume":"22","author":"R Wang","year":"2021","unstructured":"Wang R et al (2021c) Local dynamic neighborhood based outlier detection approach and its framework for large-scale datasets. Egypti Inform J 22(2):125\u2013132. https:\/\/doi.org\/10.1016\/j.eij.2020.06.001","journal-title":"Egypti Inform J"},{"issue":"16","key":"11492_CR83","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/su16229642","volume":"9642","author":"J Wang","year":"2024","unstructured":"Wang J et al (2024) Can historical accident data improve sustainable urban traffic safety? A predictive modeling study. Sustainability 9642(16):1\u201324. https:\/\/doi.org\/10.3390\/su16229642","journal-title":"Sustainability"},{"key":"11492_CR84","doi-asserted-by":"publisher","first-page":"229033","DOI":"10.1109\/ACCESS.2020.3043421","volume":"8","author":"Y Wei","year":"2020","unstructured":"Wei Y et al (2020) Large-scale outlier detection for low-cost PM$$_{10}$$ sensors. IEEE Access 8:229033\u2013229042. https:\/\/doi.org\/10.1109\/ACCESS.2020.3043421","journal-title":"IEEE Access"},{"key":"11492_CR85","doi-asserted-by":"publisher","first-page":"90283","DOI":"10.1109\/ACCESS.2023.3307190","volume":"11","author":"H Xia","year":"2023","unstructured":"Xia H et al (2023) Improved Denclue outlier detection algorithm with differential privacy and attribute fuzzy priority relation ordering. IEEE Access 11:90283\u201390297. https:\/\/doi.org\/10.1109\/ACCESS.2023.3307190","journal-title":"IEEE Access"},{"key":"11492_CR86","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.future.2022.02.007","volume":"132.C","author":"W Xiao","year":"2022","unstructured":"Xiao W, Liu H et al (2022) Attention-based deep neural network for driver behavior recognition. Future Gener Comput Syst FGCS 132.C:152\u2013161. https:\/\/doi.org\/10.1016\/j.future.2022.02.007","journal-title":"Future Gener Comput Syst FGCS"},{"key":"11492_CR87","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2023.103063","author":"W Xiao","year":"2024","unstructured":"Xiao W, Xie G et al (2024) FDAN: Fuzzy deep attention networks for driver behavior recognition. J Syst Archit. https:\/\/doi.org\/10.1016\/j.sysarc.2023.103063","journal-title":"J Syst Archit"},{"key":"11492_CR88","doi-asserted-by":"publisher","DOI":"10.1080\/15472450.2024.2425304","author":"J Xie","year":"2024","unstructured":"Xie J et al (2024) Wide human-like neural network incorporating driving styles for human-like driving intention analysis. J Intell Transp Syst: Technol, Plann, Oper. https:\/\/doi.org\/10.1080\/15472450.2024.2425304","journal-title":"J Intell Transp Syst: Technol, Plann, Oper"},{"issue":"2","key":"11492_CR89","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1109\/TAFFC.2021.3133443","volume":"14","author":"T Xu","year":"2023","unstructured":"Xu T et al (2023) E-key: an EEG-based biometric authentication and driving fatigue detection system. IEEE Trans Affect Comput 14(2):864\u2013877. https:\/\/doi.org\/10.1109\/TAFFC.2021.3133443","journal-title":"IEEE Trans Affect Comput"},{"key":"11492_CR90","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1016\/j.procs.2023.01.076","volume":"218","author":"H Yadav","year":"2023","unstructured":"Yadav H et al (2023) Experimental analysis of fuzzy clustering techniques for outlier detection. Procedia ComputV Sci 218:959\u2013968. https:\/\/doi.org\/10.1016\/j.procs.2023.01.076","journal-title":"Procedia ComputV Sci"},{"key":"11492_CR91","doi-asserted-by":"publisher","unstructured":"Yamamoto MS et al (2020) Detecting EEG outliers for BCI on the Riemannian manifold using spectral clustering. In: 2020 42nd Annual international conference of the IEEE engineering in medicine & biology society, pp 438\u2013441. https:\/\/doi.org\/10.1109\/EMBC44109.2020.9175456","DOI":"10.1109\/EMBC44109.2020.9175456"},{"key":"11492_CR92","doi-asserted-by":"publisher","first-page":"94818","DOI":"10.1109\/ACCESS.2022.3204760","volume":"10","author":"H Yang","year":"2022","unstructured":"Yang H, Li S et al (2022) Unsupervised outlier detection mechanism for tea traceability data. IEEE Access 10:94818\u201394831. https:\/\/doi.org\/10.1109\/ACCESS.2022.3204760","journal-title":"IEEE Access"},{"key":"11492_CR93","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124883","author":"X Yang","year":"2024","unstructured":"Yang X et al (2024a) Appearance-posture fusion network for distracted driving behavior recognition. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2024.124883","journal-title":"Expert Syst Appl"},{"issue":"2","key":"11492_CR94","doi-asserted-by":"publisher","first-page":"2034","DOI":"10.1109\/TITS.2023.3316203","volume":"25","author":"H Yang","year":"2024","unstructured":"Yang H, Liu H et al (2024b) Quantitative identification of driver distraction: a weakly supervised contrastive learning approach. IEEE Trans Intell Transp Syst 25(2):2034\u20132045. https:\/\/doi.org\/10.1109\/TITS.2023.3316203","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"11492_CR95","doi-asserted-by":"publisher","unstructured":"Yasser F et al (2024) Driver and vehicle unsafe behavior tracking using deep learning. In 6th International conference on computing and informatics ICCI 2024 11(4):75\u201382. https:\/\/doi.org\/10.1109\/ICCI61671.2024.10485085","DOI":"10.1109\/ICCI61671.2024.10485085"},{"key":"11492_CR96","doi-asserted-by":"publisher","unstructured":"Yeh EH et al (2024) EADD: an intelligent edge-based anomaly detection platform for car driving. pp 5208\u20135213. https:\/\/doi.org\/10.1109\/ICC51166.2024.10622518","DOI":"10.1109\/ICC51166.2024.10622518"},{"key":"11492_CR97","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6784026","author":"X Zhao","year":"2021","unstructured":"Zhao X et al (2021) Individual driver crash risk classification based on IoV data and offline consumer behavior data. Mob Inf Syst. https:\/\/doi.org\/10.1155\/2021\/6784026","journal-title":"Mob Inf Syst"},{"key":"11492_CR98","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122056","author":"Q Zhao","year":"2024","unstructured":"Zhao Q et al (2024) A driver stress detection model via data augmentation based on deep convolutional recurrent neural network. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2023.122056","journal-title":"Expert Syst Appl"},{"key":"11492_CR99","doi-asserted-by":"publisher","first-page":"42749","DOI":"10.1109\/ACCESS.2020.2977114","volume":"8","author":"R Zhu","year":"2020","unstructured":"Zhu R et al (2020) KNN-based approximate outlier detection algorithm over IoT streaming data. IEEE Access Pract Innov Open Solut 8:42749\u201342759. https:\/\/doi.org\/10.1109\/ACCESS.2020.2977114","journal-title":"IEEE Access Pract Innov Open Solut"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-026-11492-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-026-11492-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-026-11492-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T05:13:43Z","timestamp":1773378823000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-026-11492-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,29]]},"references-count":99,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["11492"],"URL":"https:\/\/doi.org\/10.1007\/s10462-026-11492-y","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,29]]},"assertion":[{"value":"5 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"93"}}