{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T23:17:01Z","timestamp":1784243821146,"version":"3.55.0"},"reference-count":52,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T00:00:00Z","timestamp":1674604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NCUT Start-up Fund"},{"name":"Open Fund of Engineering Research Center of Catastrophic Prophylaxis and Treatment of Road and Traffic Safety of Ministry of Education (Changsha University of Science and Technology)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The use of mobile phones has become one of the major threats to road safety, especially in young novice drivers. To avoid crashes induced by distraction, adaptive distraction mitigation systems have been developed that can determine how to detect a driver\u2019s distraction state. A driving simulator experiment was conducted in this paper to better explore the relationship between drivers\u2019 cognitive distractions and traffic safety, and to better analyze the mechanism of distracting effects on young drivers during the driving process. A total of 36 participants were recruited and asked to complete an n-back memory task while following the lead vehicle. Drivers\u2019 vehicle control behavior was collected, and an ANOVA was conducted on both lateral driving performance and longitudinal driving performance. Indicators from three aspects, i.e., lateral indicators only, longitudinal indicators only, and combined lateral and longitudinal indicators, were inputted into both SVM and random forest models, respectively. Results demonstrated that the SVM model with parameter optimization outperformed the random forest model in all aspects, among which the genetic algorithm had the best parameter optimization effect. For both lateral and longitudinal indicators, the identification effect of lateral indicators was better than that of longitudinal indicators, probably because drivers are more inclined to control the vehicle in lateral operation when they were cognitively distracted. Overall, the comprehensive model built in this paper can effectively identify the distracted state of drivers and provide theoretical support for control strategies of driving distraction.<\/jats:p>","DOI":"10.3390\/s23031345","type":"journal-article","created":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T01:30:30Z","timestamp":1674696630000},"page":"1345","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Young Novice Drivers\u2019 Cognitive Distraction Detection: Comparing Support Vector Machines and Random Forest Model of Vehicle Control Behavior"],"prefix":"10.3390","volume":"23","author":[{"given":"Qingwan","family":"Xue","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144, China"},{"name":"Engineering Research Center of Catastrophic Prophylaxis and Treatment of Road & Traffic Safety of Ministry of Education, Changsha University of Science & Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyue","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinghong","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Guo","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,25]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2015). Global Status Report on Road Safety 2015."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.aap.2018.09.032","article-title":"Road safety and distraction, results from a responsibility case-control study among a sample of road users interviewed at the emergency room","volume":"122","author":"Nee","year":"2019","journal-title":"Accid. Anal. Prev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.aap.2012.12.043","article-title":"Corrigendum to \u201cDriver inattention and driver distraction in serious casualty crashes: Data from the Australian National Crash In-depth Study\u201d","volume":"59","author":"Beanland","year":"2013","journal-title":"Accid. Anal. Prev."},{"key":"ref_4","unstructured":"NHTSA (2017). Distracted Driving 2015, Traffic Safety Facts Research Note."},{"key":"ref_5","unstructured":"Texas Department of Transportation (2022, September 03). Crash Records Information System. Retrieved on February 25, 2020, Available online: https:\/\/www.cris.dot.state.tx.us\/public\/Query."},{"key":"ref_6","first-page":"116","article-title":"Statistical analysis and countermeasures of traffic accidents in China","volume":"20","author":"Boyu","year":"2015","journal-title":"Contemp. Econ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lee, J.D., Regan, M.A., and Young, K.L. (2008). What drives distraction? Distraction as a breakdown of multilevel control. Driv. Distraction Theory Eff. Mitig., 41\u201356.","DOI":"10.1201\/9781420007497.ch4"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1016\/j.aap.2011.04.008","article-title":"Driver distraction and driver inattention: Definition, relationship and taxonomy","volume":"43","author":"Regan","year":"2011","journal-title":"Accid. Anal. Prev."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Regan, M.A., Lee, J.D., and Young, K. (2008). Driver Distraction, CRC Press.","DOI":"10.1201\/9781420007497"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1080\/15389588.2014.989319","article-title":"A roadside study of observable driver distractions","volume":"16","author":"Sullman","year":"2015","journal-title":"Traffic Inj. Prev."},{"key":"ref_11","first-page":"15","article-title":"Visual and cognitive distraction metrics in the age of the smart phone: A basic review","volume":"58","author":"McGehee","year":"2014","journal-title":"Ann. Adv. Automot. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.trf.2017.10.002","article-title":"Does cognitive distraction improve or degrade lane keeping performance? Analysis of time-to-line crossing safety margins","volume":"57","author":"Li","year":"2018","journal-title":"Transp. Res. Part F-Traffic Psychol. Behav."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.trc.2008.01.001","article-title":"In-vehicle data recorders for monitoring and feedback on drivers\u2019 behavior","volume":"16","author":"Toledo","year":"2008","journal-title":"Transp. Res. Part C-Emerg. Technol."},{"key":"ref_14","first-page":"20","article-title":"Effects of mobile phone distraction on drivers\u2019 reaction times","volume":"24","author":"Haque","year":"2013","journal-title":"J. Australas. Coll. Road Saf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.apergo.2016.07.011","article-title":"Distraction and task engagement: How interesting and boring information impact driving performance and subjective and physiological responses","volume":"58","author":"Horrey","year":"2017","journal-title":"Apll. Ergon."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1177\/0018720817733101","article-title":"Physiological Parameter Response to Variation of Mental Workload","volume":"60","author":"Marinescu","year":"2018","journal-title":"Hum. Factors"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.aap.2016.07.032","article-title":"Identifying cognitive distraction using steering wheel reversal rates","volume":"96","author":"Kountouriotis","year":"2016","journal-title":"Accid. Anal. Prev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s12541-011-0017-8","article-title":"Impact of Traffic Environment and Cognitive Workload on Older Drivers\u2019 Behavior in Simulated Driving","volume":"12","author":"Son","year":"2011","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/j.trf.2012.05.004","article-title":"Driver performance effects of simultaneous visual and cognitive distraction and adaptation behavior","volume":"15","author":"Kaber","year":"2012","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.trf.2005.04.014","article-title":"Sensitivity of eye-movement measures to in-vehicle task difficulty","volume":"8","author":"Victor","year":"2005","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.trf.2014.08.003","article-title":"The sensitivity of different methodologies for characterizing drivers\u2019 gaze concentration under increased cognitive demand","volume":"26","author":"Wang","year":"2014","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1177\/0018720817690639","article-title":"Effects of Cognitive Load on Driving Performance: The Cognitive Control Hypothesis","volume":"59","author":"Engstrom","year":"2017","journal-title":"Hum. Factors"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1080\/00140130701318889","article-title":"Examining cognitive interference and adaptive safety behaviours in tactical vehicle control","volume":"50","author":"Horrey","year":"2007","journal-title":"Ergonomics"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1080\/00140130010011369","article-title":"Comparative study of the effects of auditory, visual and multimodality displays on drivers\u2019 performance in advanced traveller information systems","volume":"44","author":"Liu","year":"2001","journal-title":"Ergonomics"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1518\/hfes.46.4.640.56806","article-title":"Profiles in driver distraction: Effects of cell phone conversations on younger and older drivers","volume":"46","author":"Strayer","year":"2004","journal-title":"Hum. Factors"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/B978-0-12-385527-5.00002-4","article-title":"Cognitive distraction while multitasking in the automobile","volume":"Volume 54","author":"Strayer","year":"2011","journal-title":"Psychology of Learning and Motivation"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lin, C.-T., Chen, S.-A., Ko, L.-W., and Wang, Y.-K. (August, January 31). EEG-based brain dynamics of driving distraction. Proceedings of the 2011 International Joint Conference on Neural Networks, San Jose, CA, USA.","DOI":"10.1109\/IJCNN.2011.6033401"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Miyaji, M., Kawanaka, H., and Oguri, K. (2009, January 4\u20137). Driver\u2019s cognitive distraction detection using physiological features by the adaboost. Proceedings of the 2009 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis, MO, USA.","DOI":"10.1109\/ITSC.2009.5309881"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.trf.2005.04.012","article-title":"Effects of visual and cognitive load in real and simulated motorway driving","volume":"8","author":"Johansson","year":"2005","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1016\/j.aap.2009.05.001","article-title":"Combining cognitive and visual distraction: Less than the sum of its parts","volume":"42","author":"Liang","year":"2010","journal-title":"Accid. Anal. Prev."},{"key":"ref_31","unstructured":"Torkkola, K., Massey, N., and Wood, C. (2004, January 3\u20136). Driver inattention detection through intelligent analysis of readily available sensors. Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No. 04TH8749), Washington, WA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TITS.2011.2119483","article-title":"Online Driver Distraction Detection Using Long Short-Term Memory","volume":"12","author":"Wollmer","year":"2011","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.trc.2013.10.004","article-title":"A hybrid Bayesian Network approach to detect driver cognitive distraction","volume":"38","author":"Liang","year":"2014","journal-title":"Transp. Res. Part C-Emerg. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.jelekin.2018.07.005","article-title":"A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals","volume":"42","author":"Wu","year":"2018","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.eswa.2019.01.022","article-title":"A novel reconstructed training-set SVM with roulette cooperative coevolution for financial time series classification","volume":"123","author":"Chao","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103650","DOI":"10.1016\/j.engappai.2020.103650","article-title":"A new W-SVM kernel combining PSO-neural network transformed vector and Bayesian optimized SVM in GDP forecasting","volume":"92","author":"Kouziokas","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"112988","DOI":"10.1016\/j.eswa.2019.112988","article-title":"Simultaneous feature selection and heterogeneity control for SVM classification: An application to mental workload assessment","volume":"143","author":"Maldonado","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1109\/TITS.2007.895298","article-title":"Real-time detection of driver cognitive distraction using support vector machines","volume":"8","author":"Liang","year":"2007","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_39","unstructured":"Fang, K., Wu, J., Zhu, J., and Xie, B. (2011). A Review of Technologies on Random Forests, Statistics & Information Forum."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.aap.2006.08.013","article-title":"An on-road assessment of cognitive distraction: Impacts on drivers\u2019 visual behavior and braking performance","volume":"39","author":"Harbluk","year":"2007","journal-title":"Accid. Anal. Prev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.trf.2011.06.003","article-title":"The effect of visual and cognitive distraction on driver\u2019s anticipation in a simulated car following scenario","volume":"14","author":"Muhrer","year":"2011","journal-title":"Transp. Res. Part F-Traffic Psychol. Behav."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Reimer, B., Gulash, C., Mehler, B., Foley, J.P., Arredondo, S., and Waldmann, A. (2014, January 17\u201319). The MIT AgeLab n-back: A multi-modal android application implementation. Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Seattle, WA, USA.","DOI":"10.1145\/2667239.2667293"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1080\/00140139.2011.604431","article-title":"The impact of cognitive workload on physiological arousal in young adult drivers: A field study and simulation validation","volume":"54","author":"Reimer","year":"2011","journal-title":"Ergonomics"},{"key":"ref_44","unstructured":"Hsu, C.-W., Chang, C.-C., and Lin, C.-J. (2003). A Practical Guide to Support Vector Classification, Department of Computer Science National Taiwan University."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1016\/j.csda.2007.08.015","article-title":"Empirical characterization of random forest variable importance measures","volume":"52","author":"Archer","year":"2008","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"115781","DOI":"10.1016\/j.eswa.2021.115781","article-title":"Lane change strategy analysis and recognition for intelligent driving systems based on random forest","volume":"186","author":"Sun","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1016\/j.trpro.2017.05.148","article-title":"Review of driving performance parameters critical for distracted driving research","volume":"25","author":"Papantoniou","year":"2017","journal-title":"Transp. Res. Procedia"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Sun, Q., Wang, C., Guo, Y., Yuan, W., and Fu, R. (2020). Research on a Cognitive Distraction Recognition Model for Intelligent Driving Systems Based on Real Vehicle Experiments. Sensors, 20.","DOI":"10.3390\/s20164426"},{"key":"ref_50","first-page":"43","article-title":"Driver distraction state discrimination in simulated driving environment","volume":"31","author":"Hui","year":"2018","journal-title":"China J. Highw. Transp."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1109\/TITS.2018.2814207","article-title":"Driver Sleepiness Classification Based on Physiological Data and Driving Performance From Real Road Driving","volume":"20","author":"Martensson","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_52","unstructured":"Shengqing, S. (2014). Research on the Quantitative Analysis of Motion for Execution Process of Driving Behavior under Different Degrees of Mental Workload, Beijing University of Technology."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1345\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:15:22Z","timestamp":1760120122000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,25]]},"references-count":52,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031345"],"URL":"https:\/\/doi.org\/10.3390\/s23031345","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,25]]}}}