{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:39:14Z","timestamp":1774449554669,"version":"3.50.1"},"reference-count":91,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Road accidents are on the rise worldwide, causing 1.35 million deaths per year, thus encouraging the search for solutions. The promising proposal of autonomous vehicles stands out in this regard, although fully automated driving is still far from being an achievable reality. Therefore, efforts have focused on predicting and explaining the risk of accidents using real-time telematics data. This study aims to analyze the factors, machine learning algorithms, and explainability methods most used to assess the risk of vehicle accidents based on driving behavior. A systematic review of the literature produced between 2013 and July 2023 on factors, prediction algorithms, and explainability methods to predict the risk of traffic accidents was carried out. Factors were categorized into five domains, and the most commonly used predictive algorithms and explainability methods were determined. We selected 80 articles from journals indexed in the Web of Science and Scopus databases, identifying 115 factors within the domains of environment, traffic, vehicle, driver, and management, with speed and acceleration being the most extensively examined. Regarding machine learning advancements in accident risk prediction, we identified 22 base algorithms, with convolutional neural network and gradient boosting being the most commonly used. For explainability, we discovered six methods, with random forest being the predominant choice, particularly for feature importance analysis. This study categorizes the factors affecting road accident risk, presents key prediction algorithms, and outlines methods to explain the risk assessment based on driving behavior, taking vehicle weight into consideration.<\/jats:p>","DOI":"10.3390\/computation12070131","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T03:38:27Z","timestamp":1719805107000},"page":"131","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Factors, Prediction, and Explainability of Vehicle Accident Risk Due to Driving Behavior through Machine Learning: A Systematic Literature Review, 2013\u20132023"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4109-466X","authenticated-orcid":false,"given":"Javier","family":"Lacherre","sequence":"first","affiliation":[{"name":"Faculty of Systems Engineering and Informatics, National University of San Marcos, Lima 15081, Peru"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9131-1618","authenticated-orcid":false,"given":"Jos\u00e9 Luis","family":"Castillo-Sequera","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, Polytechnic School, University of Alcala, 28871 Alcala de Henares, Spain"}]},{"given":"David","family":"Mauricio","sequence":"additional","affiliation":[{"name":"Faculty of Systems Engineering and Informatics, National University of San Marcos, Lima 15081, Peru"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2018). Global Status Report on Road Safety\u2014Time for Action, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Geng, Z., Ji, X., Cao, R., Lu, M., and Qin, W. (2022). A Conflict Measures-Based Extreme Value Theory Approach to Predicting Truck Collisions and Identifying High-Risk Scenes on Two-Lane Rural Highways. Sustainability, 14.","DOI":"10.3390\/su141811212"},{"key":"ref_3","unstructured":"Naciones Unidas (2018). La Agenda 2030 y los Objetivos de Desarrollo Sostenible: Una Oportunidad para Am\u00e9rica Latina y el Caribe, Comisi\u00f3n Econ\u00f3mica para Am\u00e9rica Latina y el Caribe (CEPAL)."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"60063","DOI":"10.1109\/ACCESS.2021.3073599","article-title":"Driver Distraction Detection Methods: A Literature Review and Framework","volume":"9","author":"Kashevnik","year":"2021","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1136\/jech.29.3.190","article-title":"The Role of Driver Demerit Points and Age in the Prediction of Motor Vehicle Collisions","volume":"29","author":"Chipman","year":"1975","journal-title":"J. Epidemiol. Community Health"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Celaya-Padilla, J.M., Galv\u00e1n-Tejada, C.E., Lozano-Aguilar, J.S.A., Zanella-Calzada, L.A., Luna-Garc\u00eda, H., Galv\u00e1n-Tejada, J.I., Gamboa-Rosales, N.K., Rodriguez, A.V., and Gamboa-Rosales, H. (2019). \u201cTexting & Driving\u201d Detection Using Deep Convolutional Neural Networks. Appl. Sci., 9.","DOI":"10.3390\/app9152962"},{"key":"ref_7","unstructured":"AASHTO (2010). Highway Safety Manual, American Association of State Highway and Transportation Officials."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.iatssr.2021.02.004","article-title":"Safety Performance Functions for Low-Volume Rural Minor Collector Two-Lane Roadways","volume":"45","author":"Das","year":"2021","journal-title":"IATSS Res."},{"key":"ref_9","first-page":"11","article-title":"Crash Distribution Dataset: Development and Validation for the Undivided Rural Roads in Oromia, Ethiopia","volume":"23","author":"Tola","year":"2022","journal-title":"Transp. Telecommun. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"11747","DOI":"10.1007\/s12652-022-03734-y","article-title":"Deep Neural Network-Based Identification of Driving Risk Utilizing Driver Dependent Vehicle Driving Features: A Scheme for Critical Infrastructure Protection","volume":"14","author":"Halim","year":"2023","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.aap.2019.05.005","article-title":"A Feature Learning Approach Based on XGBoost for Driving Assessment and Risk Prediction","volume":"129","author":"Shi","year":"2019","journal-title":"Accid. Anal. Prev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.trc.2019.05.042","article-title":"A Machine Learning Based Personalized System for Driving State Recognition","volume":"105","author":"Yi","year":"2019","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_13","unstructured":"(2022, May 20). Observatorio Nacional de Seguridad Vial Bolet\u00edn Estad\u00edstico de Siniestralidad Vial, 2021. Available online: https:\/\/www.onsv.gob.pe\/post\/boletin-estadistico-de-siniestralidad-vial-2021\/."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106477","DOI":"10.1016\/j.aap.2021.106477","article-title":"Aggressive Driving Behavior Prediction Considering Driver\u2019s Intention Based on Multivariate-Temporal Feature Data","volume":"164","author":"Xu","year":"2022","journal-title":"Accid. Anal. Prev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1109\/TITS.2022.3201378","article-title":"A Deep Multichannel Network Model for Driving Behavior Risk Classification","volume":"24","author":"Li","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103192","DOI":"10.1016\/j.ergon.2021.103192","article-title":"Analysis of Truck Drivers\u2019 Unsafe Driving Behaviors Using Four Machine Learning Methods","volume":"86","author":"Niu","year":"2021","journal-title":"Int. J. Ind. Ergon."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106153","DOI":"10.1016\/j.aap.2021.106153","article-title":"The Application of XGBoost and SHAP to Examining the Factors in Freight Truck-Related Crashes: An Exploratory Analysis","volume":"158","author":"Yang","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"57212","DOI":"10.1109\/ACCESS.2022.3165570","article-title":"An Expressway Driving Stress Prediction Model Based on Vehicle, Road and Environment Features","volume":"10","author":"Zhong","year":"2022","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1049\/itr2.12053","article-title":"Evaluation of Emergency Driving Behaviour and Vehicle Collision Risk in Connected Vehicle Environment: A Deep Learning Approach","volume":"15","author":"Peng","year":"2021","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106997","DOI":"10.1016\/j.aap.2023.106997","article-title":"Using Contextual Data to Predict Risky Driving Events: A Novel Methodology from Explainable Artificial Intelligence","volume":"184","author":"Masello","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Al-refai, G., Elmoaqet, H., and Ryalat, M. (2022). In-Vehicle Data for Predicting Road Conditions and Driving Style Using Machine Learning. Appl. Sci., 12.","DOI":"10.3390\/app12188928"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"14128","DOI":"10.1109\/ACCESS.2023.3243865","article-title":"Driver Behavior Classification: A Systematic Literature Review","volume":"11","author":"Bouhsissin","year":"2023","journal-title":"IEEE Access"},{"key":"ref_23","first-page":"912","article-title":"Intelligent Collision Risk Detection in Medium-Sized Cities of Developing Countries, Using Naturalistic Driving: A Review","volume":"9","author":"Paredes","year":"2022","journal-title":"J. Traffic Transp. Eng. (Engl. Ed.)"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103312","DOI":"10.1016\/j.engappai.2019.103312","article-title":"The Application of Machine Learning Techniques for Driving Behavior Analysis: A Conceptual Framework and a Systematic Literature Review","volume":"87","author":"Elassad","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_25","first-page":"775","article-title":"Machine Learning Applied to Road Safety Modeling: A Systematic Literature Review","volume":"7","author":"Silva","year":"2020","journal-title":"J. Traffic Transp. Eng. (Engl. Ed.)"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"117639","DOI":"10.1109\/ACCESS.2021.3105520","article-title":"Dynamic Bayesian Network Modeling, Learning, and Inference: A Survey","volume":"9","author":"Shiguihara","year":"2021","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1186\/s13643-021-01626-4","article-title":"The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews","volume":"10","author":"Page","year":"2021","journal-title":"Syst. Rev."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105008","DOI":"10.1109\/ACCESS.2020.2999829","article-title":"Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges","volume":"8","author":"Alkinani","year":"2020","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"102708","DOI":"10.1016\/j.trc.2020.102708","article-title":"A Real-Time Crash Prediction Fusion Framework: An Imbalance-Aware Strategy for Collision Avoidance Systems","volume":"118","author":"Elassad","year":"2020","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106122","DOI":"10.1016\/j.aap.2021.106122","article-title":"An Integrated Methodology for Real-Time Driving Risk Status Prediction Using Naturalistic Driving Data","volume":"156","author":"Shangguan","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"214","DOI":"10.23919\/JCC.2022.02.017","article-title":"Deep Learning-Based Prediction of Traffic Accidents Risk for Internet of Vehicles","volume":"19","author":"Zhao","year":"2022","journal-title":"China Commun."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hu, Z., Zhou, J., and Zhang, E. (2023). Improving Traffic Safety through Traffic Accident Risk Assessment. Sustainability, 15.","DOI":"10.3390\/su15043748"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"20442","DOI":"10.1109\/TITS.2022.3175528","article-title":"Recognition of Trip-Based Aggressive Driving: A System Integrated With Gaussian Mixture Model Structured of Factor-Analysis, and Hierarchical Clustering","volume":"23","author":"Wang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ghandour, R., Potams, A.J., Boulkaibet, I., Neji, B., and Barakeh, Z.A. (2021). Driver Behavior Classification System Analysis Using Machine Learning Methods. Appl. Sci., 11.","DOI":"10.3390\/app112210562"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4957","DOI":"10.1109\/ACCESS.2020.3048915","article-title":"Driving Behavior Classification Based on Oversampled Signals of Smartphone Embedded Sensors Using an Optimized Stacked-LSTM Neural Networks","volume":"9","author":"Khodairy","year":"2021","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"47881","DOI":"10.1109\/ACCESS.2022.3171247","article-title":"Macroscopic Big Data Analysis and Prediction of Driving Behavior with an Adaptive Fuzzy Recurrent Neural Network on the Internet of Vehicles","volume":"10","author":"Li","year":"2022","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJSI.319314","article-title":"Road Rage and Aggressive Driving Behaviour Detection in Usage-Based Insurance Using Machine Learning","volume":"11","author":"Arumugam","year":"2023","journal-title":"Int. J. Softw. Innov."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"11308","DOI":"10.1109\/JSEN.2023.3256000","article-title":"Smartphone Inertial Measurement Unit Data Features for Analyzing Driver Driving Behavior","volume":"23","author":"Kanwal","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"106500","DOI":"10.1016\/j.aap.2021.106500","article-title":"A Proactive Lane-Changing Risk Prediction Framework Considering Driving Intention Recognition and Different Lane-Changing Patterns","volume":"164","author":"Shangguan","year":"2022","journal-title":"Accid. Anal. Prev."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Nikolaou, D., Ziakopoulos, A., Dragomanovits, A., Roussou, J., and Yannis, G. (2023). Comparing Machine Learning Techniques for Predictions of Motorway Segment Crash Risk Level. Safety, 9.","DOI":"10.3390\/safety9020032"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"107063","DOI":"10.1016\/j.aap.2023.107063","article-title":"Modeling Driver\u2019s Evasive Behavior during Safety\u2013Critical Lane Changes: Two-Dimensional Time-to-Collision and Deep Reinforcement Learning","volume":"186","author":"Guo","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1108\/JICV-07-2021-0008","article-title":"Using Naturalistic Driving Data to Identify Driving Style Based on Longitudinal Driving Operation Conditions","volume":"5","author":"Lyu","year":"2022","journal-title":"J. Intell. Connect. Veh."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3336","DOI":"10.1109\/TITS.2020.3035700","article-title":"Robust Data-Driven Framework for Driver Behavior Profiling Using Supervised Machine Learning","volume":"23","author":"Abdelrahman","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, X., Han, J., Xiang, H., Li, H., Zhang, Y., and Li, S. (2022). A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning. Sensors, 22.","DOI":"10.3390\/s22020644"},{"key":"ref_45","first-page":"6699327","article-title":"Identification of Driver Distraction Based on SHRP2 Naturalistic Driving Study","volume":"2021","author":"Liu","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhao, L., Xu, T., Zhang, Z., and Hao, Y. (2022). Lane-Changing Recognition of Urban Expressway Exit Using Natural Driving Data. Appl. Sci., 12.","DOI":"10.3390\/app12199762"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"105822","DOI":"10.1016\/j.aap.2020.105822","article-title":"The Use of Machine Learning Improves the Assessment of Drug-Induced Driving Behaviour","volume":"148","author":"Doll","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1109\/TIV.2018.2843171","article-title":"Integrating Driving Behavior and Traffic Context Through Signal Symbolization for Data Reduction and Risky Lane Change Detection","volume":"3","author":"Yurtsever","year":"2018","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_49","first-page":"7215697","article-title":"Research on Car-Following Model Considering Driving Style","volume":"2022","author":"Wang","year":"2022","journal-title":"Math. Probl. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2083","DOI":"10.1049\/iet-its.2020.0087","article-title":"Predicting Driver Behaviour at Intersections Based on Driver Gaze and Traffic Light Recognition","volume":"14","author":"Rahman","year":"2020","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"18000","DOI":"10.1109\/ACCESS.2023.3245122","article-title":"Detection of Driver Cognitive Distraction Using Machine Learning Methods","volume":"11","author":"Misra","year":"2023","journal-title":"IEEE Access"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"887","DOI":"10.32604\/iasc.2021.016272","article-title":"Driving Pattern Profiling and Classification Using Deep Learning","volume":"28","author":"Malik","year":"2021","journal-title":"Intel. Autom. Soft Comput."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"20398","DOI":"10.1109\/TITS.2022.3193125","article-title":"Driving Risk Assessment Using Non-Negative Matrix Factorization with Driving Behavior Records","volume":"23","author":"Seo","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"114818","DOI":"10.1016\/j.eswa.2021.114818","article-title":"Machine Learning Techniques to Identify Unsafe Driving Behavior by Means of In-Vehicle Sensor Data","volume":"176","author":"Lattanzi","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1080\/01969722.2022.2059133","article-title":"New LSTM Deep Learning Algorithm for Driving Behavior Classification","volume":"54","author":"Kadri","year":"2023","journal-title":"Cybern. Syst."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"106910","DOI":"10.1016\/j.aap.2022.106910","article-title":"Predicting Collision Cases at Unsignalized Intersections Using EEG Metrics and Driving Simulator Platform","volume":"180","author":"Zhang","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1210","DOI":"10.1049\/iet-its.2018.5172","article-title":"Real-Time Detection of Distracted Driving Based on Deep Learning","volume":"12","author":"Tran","year":"2018","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_58","first-page":"238","article-title":"Real-Time Distraction Detection from Driving Data Based Personal Driving Model Using Deep Learning","volume":"20","author":"Nakano","year":"2022","journal-title":"Int. J. Intell. Transp. Syst. Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1109\/TITS.2019.2910157","article-title":"Forecasting Markers of Habitual Driving Behaviors Associated with Crash Risk","volume":"21","author":"Panagopoulos","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"106836","DOI":"10.1016\/j.aap.2022.106836","article-title":"Real-Time Driving Risk Assessment Using Deep Learning with XGBoost","volume":"178","author":"Shi","year":"2022","journal-title":"Accid. Anal. Prev."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.1109\/TMC.2019.2962764","article-title":"GazMon: Eye Gazing Enabled Driving Behavior Monitoring and Prediction","volume":"20","author":"Fan","year":"2021","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Albadawi, Y., AlRedhaei, A., and Takruri, M. (2023). Real-Time Machine Learning-Based Driver Drowsiness Detection Using Visual Features. J. Imaging, 9.","DOI":"10.3390\/jimaging9050091"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1007\/s11760-019-01589-z","article-title":"Distracted Driver Classification Using Deep Learning","volume":"14","author":"Alotaibi","year":"2020","journal-title":"Signal Image Video Process."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.smhl.2018.07.022","article-title":"ECG-Based Driver Inattention Identification during Naturalistic Driving Using Mel-Frequency Cepstrum 2-D Transform and Convolutional Neural Networks","volume":"9\u201310","author":"Taherisadr","year":"2018","journal-title":"Smart Health"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"16900","DOI":"10.1007\/s10489-022-03328-3","article-title":"Driving Maneuver Classification from Time Series Data: A Rule Based Machine Learning Approach","volume":"52","author":"Haque","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"6943","DOI":"10.1109\/TVT.2020.2993247","article-title":"Abnormal Driving Detection with Normalized Driving Behavior Data: A Deep Learning Approach","volume":"69","author":"Hu","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Jahan, I., Uddin, K.M.A., Murad, S.A., Miah, M.S.U., Khan, T.Z., Masud, M., Aljahdali, S., and Bairagi, A.K. (2023). 4D: A Real-Time Driver Drowsiness Detector Using Deep Learning. Electronics, 12.","DOI":"10.3390\/electronics12010235"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Khan, T., Choi, G., and Lee, S. (2023). EFFNet-CA: An Efficient Driver Distraction Detection Based on Multiscale Features Extractions and Channel Attention Mechanism. Sensors, 23.","DOI":"10.3390\/s23083835"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Abosaq, H.A., Ramzan, M., Althobiani, F., Abid, A., Aamir, K.M., Abdushkour, H., Irfan, M., Gommosani, M.E., Ghonaim, S.M., and Shamji, V.R. (2023). Unusual Driver Behavior Detection in Videos Using Deep Learning Models. Sensors, 23.","DOI":"10.3390\/s23010311"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"109335","DOI":"10.1109\/ACCESS.2020.3001159","article-title":"HCF: A Hybrid CNN Framework for Behavior Detection of Distracted Drivers","volume":"8","author":"Huang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"83138","DOI":"10.1109\/ACCESS.2022.3197146","article-title":"AB-DLM: An Improved Deep Learning Model Based on Attention Mechanism and BiFPN for Driver Distraction Behavior Detection","volume":"10","author":"Li","year":"2022","journal-title":"IEEE Access"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.aej.2022.12.009","article-title":"Real-Time Driver Distraction Recognition: A Hybrid Genetic Deep Network Based Approach","volume":"66","author":"Aljohani","year":"2023","journal-title":"Alex. Eng. J."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Lin, Y.C., Cao, D.X., Fu, Z.H., Huang, Y.M., and Song, Y.Y. (2022). A Lightweight Attention-Based Network towards Distracted Driving Behavior Recognition. Appl. Sci., 12.","DOI":"10.3390\/app12094191"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"117837","DOI":"10.1016\/j.eswa.2022.117837","article-title":"Real-Time Vehicular Accident Prevention System Using Deep Learning Architecture","volume":"206","author":"Kabir","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_75","first-page":"200075","article-title":"Automatic Driver Distraction Detection Using Deep Convolutional Neural Networks","volume":"14","author":"Hossain","year":"2022","journal-title":"Intell. Syst. Appl."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"77523","DOI":"10.1109\/ACCESS.2022.3186674","article-title":"Innovative Framework for Distracted-Driving Alert System Based on Deep Learning","volume":"10","author":"Lin","year":"2022","journal-title":"IEEE Access"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Xiao, W., Liu, H., Ma, Z., Chen, W., Sun, C., and Shi, B. (2022). Fatigue Driving Recognition Method Based on Multi-Scale Facial Landmark Detector. Electronics, 11.","DOI":"10.3390\/electronics11244103"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"168080","DOI":"10.1109\/ACCESS.2021.3133797","article-title":"Robust Deep Learning-Based Driver Distraction Detection and Classification","volume":"9","author":"Ezzouhri","year":"2021","journal-title":"IEEE Access"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/MITS.2021.3120279","article-title":"A Context-Aware Framework for Risky Driving Behavior Evaluation Based on Trajectory Data","volume":"15","author":"Xue","year":"2023","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"12338","DOI":"10.1109\/JIOT.2021.3135512","article-title":"SafeDriving: An Effective Abnormal Driving Behavior Detection System Based on EMG Signals","volume":"9","author":"Fan","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.neucom.2020.09.023","article-title":"Deep Unsupervised Multi-Modal Fusion Network for Detecting Driver Distraction","volume":"421","author":"Zhang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_82","first-page":"440","article-title":"Integration of Ensemble Variant CNN with Architecture Modified LSTM for Distracted Driver Detection","volume":"13","author":"Boucetta","year":"2022","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Safarov, F., Akhmedov, F., Abdusalomov, A.B., Nasimov, R., and Cho, Y.I. (2023). Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety. Sensors, 23.","DOI":"10.3390\/s23146459"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.3390\/vehicles4040069","article-title":"Adaptive Driving Style Classification through Transfer Learning with Synthetic Oversampling","volume":"4","author":"Jardin","year":"2022","journal-title":"Vehicles"},{"key":"ref_85","first-page":"1594","article-title":"FPT: Fine-Grained Detection of Driver Distraction Based on the Feature Pyramid Vision Transformer","volume":"24","author":"Wang","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.inffus.2022.08.009","article-title":"Distracted Driving Detection Based on the Fusion of Deep Learning and Causal Reasoning","volume":"89","author":"Ping","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"2316","DOI":"10.1109\/TITS.2017.2768527","article-title":"Application of Real Field Connected Vehicle Data for Aggressive Driving Identification on Horizontal Curves","volume":"19","author":"Jahangiri","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Siddiqui, H.U.R., Saleem, A.A., Brown, R., Bademci, B., Lee, E., Rustam, F., and Dudley, S. (2021). Non-Invasive Driver Drowsiness Detection System. Sensors, 21.","DOI":"10.3390\/s21144833"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"107072","DOI":"10.1016\/j.aap.2023.107072","article-title":"A Proactive Crash Risk Prediction Framework for Lane-Changing Behavior Incorporating Individual Driving Styles","volume":"188","author":"Zhang","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Cai, B., and Di, Q. (2023). Different Forecasting Model Comparison for Near Future Crash Prediction. Appl. Sci., 13.","DOI":"10.3390\/app13020759"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.pmcj.2017.06.003","article-title":"A Smartphone Based Technique to Monitor Driving Behavior Using DTW and Crowdsensing","volume":"40","author":"Singh","year":"2017","journal-title":"Pervasive Mob. Comput."}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/12\/7\/131\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:07:32Z","timestamp":1760108852000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/12\/7\/131"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,28]]},"references-count":91,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["computation12070131"],"URL":"https:\/\/doi.org\/10.3390\/computation12070131","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,28]]}}}