{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T05:21:05Z","timestamp":1788326465482,"version":"build-2803163510"},"reference-count":54,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009110","name":"Natural Science Foundation of Xinjiang Uygur Autonomous Region","doi-asserted-by":"publisher","award":["2024D01A65"],"award-info":[{"award-number":["2024D01A65"]}],"id":[{"id":"10.13039\/100009110","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Program of Xinjiang Uygur Autonomous Region","award":["2022B01013-2"],"award-info":[{"award-number":["2022B01013-2"]}]},{"DOI":"10.13039\/501100012142","name":"Xinjiang Agricultural University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100012142","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/04qjh2h11","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Continuous eye tracking is difficult to deploy in production vehicles, while eye-movement-derived proxy labels are susceptible to same-source circularity and road-type confounding. We propose a driver-independent, confounder-aware cross-source framework in which the eye-movement-derived visual load index (VLI) proxy is constructed within training folds, whereas held-out prediction uses only vehicle kinematics and an optional driving-style prior. Evaluation used naturalistic data from 33 drivers, comprising 24,437 windows along a 48.4 km route, under driver-grouped cross-validation with driver-level bootstrap confidence intervals and control and increment tests. The vehicle-only model achieved an AUC of 0.589 across held-out drivers, rising to 0.633 with the style prior. After controlling for road type, vehicle features improved AUC by 0.042 (95% CI: 0.006\u20130.078), indicating measurable information beyond the binary road-type proxy; the style increment remained exploratory because its confidence interval included zero. Future-state prediction converged to a persistence baseline at 20 s. The VLI was not externally validated against an independent subjective or physiological criterion; accordingly, the findings are limited to cross-source prediction of an eye-movement-derived proxy. Across five architectures, this cross-source pattern was directionally consistent, supporting prospective evaluation of vehicle kinematics as a complementary sensing channel in multisource driver-monitoring systems.<\/jats:p>","DOI":"10.3390\/s26175521","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T03:19:26Z","timestamp":1788232766000},"page":"5521","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Source Prediction of a Visual Load Index from Vehicle Kinematic Features: A Driver-Independent Validation Study"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7064-0316","authenticated-orcid":false,"given":"Tursun","family":"Mamat","sequence":"first","affiliation":[{"name":"School of Transportation and Logistics Engineering, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Intelligent Transportation Engineering Research Center, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Xinjiang Key Laboratory of Transportation and Logistics Engineering, Xinjiang Agricultural University, Urumqi 830052, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"given":"Siyi","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics Engineering, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Intelligent Transportation Engineering Research Center, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Xinjiang Key Laboratory of Transportation and Logistics Engineering, Xinjiang Agricultural University, Urumqi 830052, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0195-9139","authenticated-orcid":false,"given":"Chunguang","family":"He","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics Engineering, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Intelligent Transportation Engineering Research Center, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Xinjiang Key Laboratory of Transportation and Logistics Engineering, Xinjiang Agricultural University, Urumqi 830052, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]},{"given":"Jiake","family":"Wuyuncaicike","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics Engineering, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Intelligent Transportation Engineering Research Center, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Xinjiang Key Laboratory of Transportation and Logistics Engineering, Xinjiang Agricultural University, Urumqi 830052, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2854","DOI":"10.1016\/j.promfg.2015.07.783","article-title":"Review of Eye-related Measures of Drivers\u2019 Mental Workload","volume":"3","author":"Marquart","year":"2015","journal-title":"Procedia Manuf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1037\/1076-898X.6.1.31","article-title":"Effects of verbal and spatial-imagery tasks on eye fixations while driving","volume":"6","author":"Recarte","year":"2000","journal-title":"J. Exp. Psychol. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1037\/1076-898X.9.2.119","article-title":"Mental workload while driving: Effects on visual search, discrimination, and decision making","volume":"9","author":"Recarte","year":"2003","journal-title":"J. Exp. Psychol. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.trf.2010.12.001","article-title":"Driver workload and eye blink duration","volume":"14","author":"Benedetto","year":"2011","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0001-6918(90)90067-P","article-title":"Eye movement indices of mental workload","volume":"75","author":"May","year":"1990","journal-title":"Acta Psychol."},{"key":"ref_6","first-page":"20","article-title":"An Investigation on Vehicle Trajectory Characteristics at Exit and Entrance of High-density Interchanges Based on Naturalistic Driving Data","volume":"41","author":"Xu","year":"2023","journal-title":"J. Transp. Inf. Saf."},{"key":"ref_7","first-page":"28","article-title":"Drivers\u2019 Mental Load Characteristics at the Entrance and Exit of High-density Interchanges Based on Heart Rate Variability","volume":"42","author":"Mu","year":"2024","journal-title":"J. Transp. Inf. Saf."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Jiang, P., Wang, S., Cheng, S., Xu, J., and Liu, Y. (2024). Study on the Driver Visual Workload in High-Density Interchange-Merging Areas Based on a Field Driving Test. Sensors, 24.","DOI":"10.3390\/s24196247"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1109\/TNSRE.2023.3267114","article-title":"Fusion of EEG and Eye Blink Analysis for Detection of Driver Fatigue","volume":"31","author":"Shahbakhti","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1177\/0018720812442086","article-title":"Sensitivity of physiological measures for detecting systematic variations in cognitive demand from a working memory task: An on-road study across three age groups","volume":"54","author":"Mehler","year":"2012","journal-title":"Hum. Factors"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.neubiorev.2012.10.003","article-title":"Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness","volume":"44","author":"Borghini","year":"2014","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1016\/j.aap.2009.06.001","article-title":"Monitoring drivers\u2019 mental workload in driving simulators using physiological measures","volume":"42","author":"Brookhuis","year":"2010","journal-title":"Accid. Anal. Prev."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Palinko, O., Kun, A.L., Shyrokov, A., and Heeman, P. (2010, January 22\u201324). Estimating cognitive load using remote eye tracking in a driving simulator. Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications (ETRA \u201910), Austin, TX, USA.","DOI":"10.1145\/1743666.1743701"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.trf.2023.07.012","article-title":"Comparing driver reaction and mental workload of visual and auditory take-over request from perspective of driver characteristics and eye-tracking metrics","volume":"97","author":"Chen","year":"2023","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_15","first-page":"100087","article-title":"Evaluating driver cognitive distraction by eye tracking: From simulator to driving","volume":"4","author":"Le","year":"2020","journal-title":"Transp. Res. Interdiscip. Perspect."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1007\/s12541-013-0179-7","article-title":"Identification of driver cognitive workload using support vector machines with driving performance, physiology and eye movement in a driving simulator","volume":"14","author":"Son","year":"2013","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.trf.2022.05.004","article-title":"Comparing eye-tracking metrics of mental workload caused by NDRTs in semi-autonomous driving","volume":"89","author":"Chen","year":"2022","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/j.aap.2009.03.019","article-title":"Mental workload when driving in a simulator: Effects of age and driving complexity","volume":"41","author":"Cantin","year":"2009","journal-title":"Accid. Anal. Prev."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Paxion, J., Galy, E., and Berthelon, C. (2014). Mental workload and driving. Front. Psychol., 5.","DOI":"10.3389\/fpsyg.2014.01344"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/S0001-4575(03)00010-1","article-title":"The multidimensional driving style inventory\u2014Scale construct and validation","volume":"36","author":"Mikulincer","year":"2004","journal-title":"Accid. Anal. Prev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1037\/0033-2909.113.2.279","article-title":"Behavioral correlates of individual differences in road-traffic crash risk: An examination of methods and findings","volume":"113","author":"Elander","year":"1993","journal-title":"Psychol. Bull."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1177\/0018720815591313","article-title":"A Review of Research on Driving Styles and Road Safety","volume":"57","author":"Sagberg","year":"2015","journal-title":"Hum. Factors"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"9085238","DOI":"10.1155\/2019\/9085238","article-title":"Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data","volume":"2019","author":"Xue","year":"2019","journal-title":"J. Adv. Transp."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cordero, J., Aguilar, J., Aguilar, K., Ch\u00e1vez, D., and Puerto, E. (2020). Recognition of the Driving Style in Vehicle Drivers. Sensors, 20.","DOI":"10.3390\/s20092597"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ullah, S., and Kim, D.-H. (2020). Lightweight Driver Behavior Identification Model with Sparse Learning on In-Vehicle CAN-BUS Sensor Data. Sensors, 20.","DOI":"10.3390\/s20185030"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105908","DOI":"10.1016\/j.aap.2020.105908","article-title":"Analyzing driver behavior under naturalistic driving conditions: A review","volume":"150","author":"Singh","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1109\/OJITS.2021.3109039","article-title":"A Review of UTDrive Studies: Learning Driver Behavior from Naturalistic Driving Data","volume":"2","author":"Liu","year":"2021","journal-title":"IEEE Open J. Intell. Transp. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"107779","DOI":"10.1016\/j.aap.2024.107779","article-title":"Characterizing driver behavior using naturalistic driving data","volume":"208","author":"Lee","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.trf.2016.10.019","article-title":"Effects of driving anger on driver behavior\u2014Results from naturalistic driving data","volume":"45","author":"Precht","year":"2017","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.jsr.2020.03.008","article-title":"Using SHRP2 naturalistic driving data to examine driver speeding behavior","volume":"73","author":"Richard","year":"2020","journal-title":"J. Saf. Res."},{"key":"ref_32","first-page":"78","article-title":"Learning the Driver Acceleration\/Deceleration Behavior under High-Speed Environments from Naturalistic Driving Data","volume":"14","author":"Liu","year":"2022","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3325","DOI":"10.1109\/TITS.2015.2449837","article-title":"An Unsupervised Approach for Inferring Driver Behavior from Naturalistic Driving Data","volume":"16","author":"Bender","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201916), San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_35","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the 31st Conference on Neural Information Processing Systems (NeurIPS 2017), Long Beach, CA, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014, January 25\u201329). Learning Phrase Representations using RNN Encoder\u2013Decoder for Statistical Machine Translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_38","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017, January 4\u20139). A Unified Approach to Interpreting Model Predictions. Proceedings of the 31st Conference on Neural Information Processing Systems (NeurIPS 2017), Long Beach, CA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3047","DOI":"10.1109\/TIV.2023.3313419","article-title":"Driver Profiling and Bayesian Workload Estimation Using Naturalistic Peripheral Detection Study Data","volume":"9","author":"Caber","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"20227","DOI":"10.1109\/TITS.2024.3451144","article-title":"Quantitative Estimation of Driver Cognitive Workload: A Dual-Stage Learning Approach","volume":"25","author":"Zhu","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Shao, X., Ma, X., Chen, F., and Pan, X. (2026). Multimodal Machine Learning Framework for Driver Mental Workload Classification: A Comparative and Interpretable Approach. Appl. Sci., 16.","DOI":"10.3390\/app16073581"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"121729","DOI":"10.1016\/j.eswa.2023.121729","article-title":"A Multilayer Stacking Method Base on RFE-SHAP Feature Selection Strategy for Recognition of Driver\u2019s Mental Load and Emotional State","volume":"238","author":"Huang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Salvucci, D.D., and Goldberg, J.H. (2000, January 6\u20138). Identifying fixations and saccades in eye-tracking protocols. Proceedings of the 2000 Symposium on Eye Tracking Research & Applications (ETRA \u201900), Palm Beach Gardens, FL, USA.","DOI":"10.1145\/355017.355028"},{"key":"ref_44","unstructured":"(2009). Specification for Design of Urban Expressway (Standard No. CJJ 129\u20132009)."},{"key":"ref_45","unstructured":"(2014). Technical Standard of Highway Engineering (Standard No. JTG B01\u20132014)."},{"key":"ref_46","unstructured":"National Academies of Sciences, Engineering, and Medicine (2022). Highway Capacity Manual: A Guide for Multimodal Mobility Analysis, The National Academies Press. [7th ed.]."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A Mathematical Theory of Communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/0305-0548(94)00059-H","article-title":"Determining objective weights in multiple criteria problems: The CRITIC method","volume":"22","author":"Diakoulaki","year":"1995","journal-title":"Comput. Oper. Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1007\/s11069-015-1645-6","article-title":"A fuzzy comprehensive evaluation model for flood risk based on the combination weight of game theory","volume":"77","author":"Lai","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_50","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","volume":"Volume 1","author":"MacQueen","year":"1967","journal-title":"Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Saito, T., and Rehmsmeier, M. (2015). The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0118432"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Efron, B., and Tibshirani, R.J. (1994). An Introduction to the Bootstrap, Chapman & Hall\/CRC.","DOI":"10.1201\/9780429246593"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5521\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T04:34:55Z","timestamp":1788323695000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5521"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,31]]},"references-count":54,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26175521"],"URL":"https:\/\/doi.org\/10.3390\/s26175521","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,31]]}}}