{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T13:21:29Z","timestamp":1783516889412,"version":"3.55.0"},"reference-count":53,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Identifying driving styles is essential for personalizing driving assistance systems and enhancing intelligent transportation services. However, existing approaches predominantly rely on experience-driven feature engineering and annotated data, limiting objectivity and hindering the exploitation of unlabeled naturalistic driving datasets. To address these limitations, this paper proposes an unsupervised framework for driving style identification from naturalistic driving data through context normalization. A Constrained Convolutional Autoencoder (CCAE) integrated with a global self-attention mechanism is developed to map unlabeled driving sequences onto a standardized dynamic reference defined by the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). This process extracts Driving Adaptability Characteristics (DACs) as WLTC-anchored latent representations that characterize normalized driver-specific response patterns across heterogeneous naturalistic contexts. To ensure feature robustness, frequency-domain refinement is applied to eliminate high-frequency noise. The extracted DAC sequences are subsequently partitioned into distinct driving styles using a kernel-mapped clustering algorithm. To evaluate the external relevance and physical interpretability of the identified styles, actual vehicle accident records and raw CAN-bus feature backtracking are introduced as validation evidence. The results show that the identified driving styles exhibit different historical accident probabilities. The proposed CCAE model achieves clearer cluster-level differentiation than traditional feature engineering and unconstrained deep learning models, and the ablation analysis confirms the contribution of the WLTC-based constraint. These findings indicate that the context-normalization framework can extract interpretable and externally relevant driving style representations from unlabeled naturalistic data.<\/jats:p>","DOI":"10.3390\/s26134309","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T13:19:31Z","timestamp":1783430371000},"page":"4309","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised Identification of Driving Styles from Naturalistic Driving Data Through a Context-Normalized Framework"],"prefix":"10.3390","volume":"26","author":[{"given":"Cunzhi","family":"Xu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Fluid Power Components and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"},{"name":"Institute of Manufacturing Technology and Systems Engineering, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reuben S.K.","family":"Agbozo","sequence":"additional","affiliation":[{"name":"Institute of Manufacturing Technology and Systems Engineering, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Manufacturing Technology and Systems Engineering, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fluid Power Components and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"},{"name":"Institute of Manufacturing Technology and Systems Engineering, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1646-6166","authenticated-orcid":false,"given":"Tao","family":"Peng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fluid Power Components and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"},{"name":"Institute of Manufacturing Technology and Systems Engineering, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renzhong","family":"Tang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fluid Power Components and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"},{"name":"Institute of Manufacturing Technology and Systems Engineering, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,7]]},"reference":[{"key":"ref_1","first-page":"737","article-title":"Assessing the Relationship between Driving Skill, Driving Behavior and Driving Aggressiveness","volume":"14","author":"Yang","year":"2022","journal-title":"J. Transp. Saf. Secur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1016\/j.trpro.2017.12.024","article-title":"How Drivers\u2019 Characteristics Can Affect Driving Style","volume":"27","author":"Eboli","year":"2017","journal-title":"Transp. Res. Procedia"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107022","DOI":"10.1016\/j.aap.2023.107022","article-title":"Feature Selection for Driving Style and Skill Clustering Using Naturalistic Driving Data and Driving Behavior Questionnaire","volume":"185","author":"Chen","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"ref_4","unstructured":"World Health Organization (2015). Global Status Report on Road Safety 2015, World Health Organization."},{"key":"ref_5","unstructured":"CRTAS (2010). China Road Traffic Accidents Statistics."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1186\/s40537-019-0249-5","article-title":"A Survey on Driving Behavior Analysis in Usage Based Insurance Using Big Data","volume":"6","author":"Arumugam","year":"2019","journal-title":"J. Big Data"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107269","DOI":"10.1016\/j.aap.2023.107269","article-title":"Context-Aware Driver Risk Prediction with Telematics Data","volume":"192","author":"Moosavi","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nai, W., Yang, Z., Wei, Y., Sang, J., Wang, J., Wang, Z., and Mo, P. (2022). A Comprehensive Review of Driving Style Evaluation Approaches and Product Designs Applied to Vehicle Usage-Based Insurance. Sustainability, 14.","DOI":"10.3390\/su14137705"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sakairi, M., and Togami, M. (2010, January 1\u20134). Use of Water Cluster Detector for Preventing Drunk and Drowsy Driving. Proceedings of the 2010 IEEE SENSORS, Waikoloa, HI, USA.","DOI":"10.1109\/ICSENS.2010.5690673"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jeong, I.C., Lee, D.H., Park, S.W., Ko, J.I., and Yoon, H.R. (2007, January 13\u201315). Automobile Driver\u2019s Stress Index Provision System That Utilizes Electrocardiogram. Proceedings of the 2007 IEEE Intelligent Vehicles Symposium, Istanbul, Turkey.","DOI":"10.1109\/IVS.2007.4290190"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s11571-018-9496-y","article-title":"EEG Classification of Driver Mental States by Deep Learning","volume":"12","author":"Zeng","year":"2018","journal-title":"Cogn. Neurodyn."},{"key":"ref_12","first-page":"572","article-title":"The Car-Following Model Based on the Drivers\u2019 Psychological Characteristics","volume":"20","author":"Kong","year":"2022","journal-title":"Int. J. Intell. Transp. Syst. Res."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ed-Doughmi, Y., Idrissi, N., and Hbali, Y. (2020). Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network. J. Imaging, 6.","DOI":"10.3390\/jimaging6030008"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6473","DOI":"10.1109\/TITS.2021.3058035","article-title":"Lane Change Prediction with an Echo State Network and Recurrent Neural Network in the Urban Area","volume":"23","author":"Griesbach","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lyu, Y., Wang, Y., Liu, H., Dong, X., He, Y., and Ren, Y. (2025). A Driving-Preference-Aware Framework for Vehicle Lane Change Prediction. Sensors, 25.","DOI":"10.3390\/s25175342"},{"key":"ref_16","first-page":"1343","article-title":"Identification of Dangerous Driving Behaviors Based on Neural Network and Bayesian Filter","volume":"846\u2013847","author":"Xu","year":"2014","journal-title":"Adv. Mater. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1177\/0018720813515272","article-title":"Steering in a Random Forest: Ensemble Learning for Detecting Drowsiness-Related Lane Departures","volume":"56","author":"McDonald","year":"2014","journal-title":"Hum. Factors"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1177\/03611981211018695","article-title":"Enhancing the Performance of a Model to Predict Driving Distraction with the Random Forest Classifier","volume":"2675","author":"Ahangari","year":"2021","journal-title":"Transp. Res. Rec."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s13638-020-1639-2","article-title":"A Driver\u2019s Car-Following Behavior Prediction Model Based on Multi-Sensors Data","volume":"2020","author":"Wang","year":"2020","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wu, Z., Li, F., Xie, C., Ren, T., Chen, J., and Liu, L. (2019). A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data. Sensors, 19.","DOI":"10.3390\/s19061356"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Allen, R.W., Chrstos, J.P., Aponso, B.L., and Lee, D. (2002). Driver\/Vehicle Modeling and Simulation, SAE International.","DOI":"10.4271\/2002-01-1568"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106096","DOI":"10.1016\/j.aap.2021.106096","article-title":"Driving Style Recognition and Comparisons among Driving Tasks Based on Driver Behavior in the Online Car-Hailing Industry","volume":"154","author":"Ma","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_23","first-page":"407","article-title":"Recognition Method of Drinking-Driving Behaviors Based on PCA and RBF Neural Network","volume":"30","author":"Sun","year":"2018","journal-title":"Promet-Traffic Transp."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Deng, Q., Wang, J., and S\u00f6ffker, D. (2018, January 26\u201330). Prediction of Human Driver Behaviors Based on an Improved HMM Approach. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500717"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"113240","DOI":"10.1016\/j.eswa.2020.113240","article-title":"Driver Behavior Detection and Classification Using Deep Convolutional Neural Networks","volume":"149","author":"Shahverdy","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"119254","DOI":"10.1016\/j.eswa.2022.119254","article-title":"Vehicles Driving Behavior Recognition Based on Transfer Learning","volume":"213","author":"Chen","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e6676092","DOI":"10.1155\/2021\/6676092","article-title":"Driver Lane-Changing Behavior Prediction Based on Deep Learning","volume":"2021","author":"Wei","year":"2021","journal-title":"J. Adv. Transp."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zou, Y., Ding, L., Zhang, H., Zhu, T., and Wu, L. (2022). Vehicle Acceleration Prediction Based on Machine Learning Models and Driving Behavior Analysis. Appl. Sci., 12.","DOI":"10.3390\/app12105259"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhao, C., Gong, J., Lu, C., Xiong, G., and Mei, W. (2017, January 16\u201319). Speed and Steering Angle Prediction for Intelligent Vehicles Based on Deep Belief Network. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317929"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhao, C., Lu, C., Wei, L., and Gong, J. (2021). Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network. Sensors, 21.","DOI":"10.3390\/s21248498"},{"key":"ref_31","first-page":"9","article-title":"Fully Convolutional Variational Autoencoder For Feature Extraction of Fire Detection System","volume":"13","author":"Nugroho","year":"2020","journal-title":"J. Ilmu Komput. Dan Inf."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Keser, R., and T\u00f6reyin, B. (2019, January 26\u201329). Autoencoder Based Dimensionality Reduction of Feature Vectors for Object Recognition. Proceedings of the 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Sorrento, Italy.","DOI":"10.1109\/SITIS.2019.00097"},{"key":"ref_33","first-page":"96","article-title":"Deep Convolutional Autoencoders as Generic Feature Extractors in Seismological Applications","volume":"2","author":"Kong","year":"2021","journal-title":"Artif. Intell. Geosci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chakraborty, N., Hasan, A., Liu, S., Ji, T., Liang, W., McPherson, D.L., and Driggs-Campbell, K. (2023, January 30). Structural Attention-Based Recurrent Variational Autoencoder for Highway Vehicle Anomaly Detection. Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems; International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, USA.","DOI":"10.65109\/KQXF3853"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Johnson, D.A., and Trivedi, M.M. (2011, January 5\u20137). Driving Style Recognition Using a Smartphone as a Sensor Platform. Proceedings of the 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington, DC, USA.","DOI":"10.1109\/ITSC.2011.6083078"},{"key":"ref_36","first-page":"103","article-title":"Investigation of Route-Independent Aggressive and Safe Driving Features Obtained from Accelerometer Signals","volume":"9","author":"Zylius","year":"2017","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"102917","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":"ref_38","doi-asserted-by":"crossref","first-page":"16203","DOI":"10.1109\/ACCESS.2023.3245146","article-title":"CNN-LSTM Driving Style Classification Model Based on Driver Operation Time Series Data","volume":"11","author":"Cai","year":"2023","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3065","DOI":"10.1109\/LRA.2021.3062309","article-title":"IDE-Net: Interactive Driving Event and Pattern Extraction From Human Data","volume":"6","author":"Jia","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"413","DOI":"10.18280\/ijtdi.080305","article-title":"Driver Identification Via Hybrid Model and Few-Shot Technique Based CAN-BUS Data","volume":"8","author":"Jabbar","year":"2024","journal-title":"Int. J. Transp. Dev. Integr."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.56578\/mvbb010101","article-title":"Enhancing Transportation Safety: An Integrated Approach Using FLFS and OSNCA for Advanced Driving Behavior Analysis","volume":"1","author":"Malik","year":"2023","journal-title":"MindVanguard Beyond Behav."},{"key":"ref_42","unstructured":"Wikipedia Contributors (2026, July 02). Autoencoder. Available online: https:\/\/en.wikipedia.org\/wiki\/Autoencoder."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/0098-1354(92)80051-A","article-title":"Autoassociative Neural Networks","volume":"16","author":"Kramer","year":"1992","journal-title":"Comput. Chem. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1109\/OJITS.2022.3149474","article-title":"Classification and Evaluation of Driving Behavior Safety Levels: A Driving Simulation Study","volume":"3","author":"Yang","year":"2022","journal-title":"IEEE Open J. Intell. Transp. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Guan, H., Song, Y., Lu, P., Dai, C., Gao, S., Zhan, J., Duan, C., Liao, Y., and Zhao, B. (2025). Personalized Steering Feel Control Based on Driving Style Recognition and Closed-Loop Motion Regulation. Sensors, 25.","DOI":"10.3390\/s25247686"},{"key":"ref_46","first-page":"187","article-title":"Driving Style Classification Method for Lane Change Warning System","volume":"14","author":"Wang","year":"2014","journal-title":"J. Transp. Syst. Eng. Inf. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"115824","DOI":"10.1016\/j.energy.2019.07.154","article-title":"Adaptive Optimal Control Based on Driving Style Recognition for Plug-in Hybrid Electric Vehicle","volume":"186","author":"Guo","year":"2019","journal-title":"Energy"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"117236","DOI":"10.1016\/j.eswa.2022.117236","article-title":"A Driving-Style-Oriented Adaptive Control Strategy Based PSO-Fuzzy Expert Algorithm for a Plug-in Hybrid Electric Vehicle","volume":"201","author":"Lin","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Guo, Y., Wang, X., Huang, Y., and Xu, L. (2021). Collaborative Driving Style Classification Method Enabled by Majority Voting Ensemble Learning for Enhancing Classification Performance. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0254047"},{"key":"ref_50","unstructured":"Wikipedia Contributors (2026, July 02). Attention (Machine Learning). Available online: https:\/\/en.wikipedia.org\/wiki\/Attention_(machine_learning)."},{"key":"ref_51","unstructured":"Wikipedia Contributors (2026, July 02). Worldwide Harmonised Light Vehicles Test Procedure. Available online: https:\/\/en.wikipedia.org\/wiki\/Worldwide_Harmonised_Light_Vehicles_Test_Procedure."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"9071","DOI":"10.15282\/ijame.18.3.2021.19.0696","article-title":"The WLTC vs NEDC: A Case Study on the Impacts of Driving Cycle on Engine Performance and Fuel Consumption","volume":"18","author":"Lasocki","year":"2021","journal-title":"Int. J. Automot. Mech. Eng."},{"key":"ref_53","unstructured":"He, X. (2021). Characterization of Driving Behaviors and Its Application. [Doctoral Dissertation, Zhejiang University]."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/13\/4309\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T12:55:59Z","timestamp":1783515359000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/13\/4309"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,7]]},"references-count":53,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["s26134309"],"URL":"https:\/\/doi.org\/10.3390\/s26134309","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,7]]}}}