Intelligent System for Notifying the Driver of Distractions From Driving the Vehicle
DOI:
https://doi.org/10.18372/1990-5548.88.20959Keywords:
driving behavior, computer vision, behaviour classification, accident prevention, intelligent transport systems, ADASAbstract
The article discusses the development of a functional system that, based on the recognition of distracting behaviour by the driver, is capable of issuing warning signals that the driver is distracted from the process of driving a vehicle. The described methodology covers basic methods and procedures necessary for the creation of a precision, resistant to disturbances, and at the same time controllable system for precision stabilization of the equipment assigned for operation on moving vehicles of a wide class. As part of the study, such a system was developed that takes into account the driver's reaction time, age, gender, and duration of the trip. If a critical situation is detected, the system automatically generates a warning signal for the driver. The features of the modern approach to data processing based on neural networks are described. The implementation of such a system is aimed at improving road safety and reducing the number of accidents. The developed emergency warning system not only allows drivers to mitigate the consequences of road accidents, but also helps them to avoid them by drawing their attention to emerging dangers in a timely manner if the driver's response is insufficient or absent.
References
Alshalfan, Khalid & Zakariah, Mohammed. (2021). Detecting Driver Distraction Using Deep-Learning Approach. Computers, Materials & Continua. 68. 689-704. 10.32604/cmc.2021.015989.
Annual accident report. Available at: https://road-safety.transport.ec.europa.eu/european-road-safety-observatory/statistics-and-analysis-archive/annual-accident-report_en
Bauder M, Paula D, Pfeilschifter C, Petermeier F, Kubjatko T, Riener A, Schweiger H-G. Influences of Vehicle Communication on Human Driving Reactions: A Simulator Study on Reaction Times and Behavior for Forensic Accident Analysis. Sensors. 2024; 24(14):4481. https://doi.org/10.3390/s24144481
Bucsuházy, Kateřina & Matuchová, Eva & Zůvala, Robert & Moravcová, Pavlína & Kostíková, Martina & Mikulec, Roman. (2020). Human factors contributing to the road traffic accident occurrence. Transportation Research Procedia. 45. DOI: 555-561. 10.1016/j.trpro.2020.03.057.
Dingus, Thomas & Guo, Feng & Lee, Suzie & Antin, Jonathan & Perez, Miguel & Buchanan-King, Mindy & Hankey, Jon. (2016). Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proceedings of the National Academy of Sciences. 113. 201513271. 10.1073/pnas.1513271113.
Eraqi, Hesham & Abouelnaga, Yehya & Saad, Mohamed & Moustafa, Mohamed. (2019). Driver Distraction Identification with an Ensemble of Convolutional Neural Networks. 10.48550/arXiv.1901.09097.
Fabian Friedrichs, Bin Yang. Drowsiness Monitoring by Steering and Lane Data Based Features Under Real Driving Conditions // 18th European Signal Processing Conference, Aal-borg, Denmark, 2010.-C. 209-213.
Governors Highway Safety Association. Available at: https://www.ghsa.org
Huo, Faren & Gao, Ranran & Sun, Cong & Hou, Guanhua. (2022). Age Differences in Hazard Perception of Drivers: The Roles of Emotion. Frontiers in Psychology. 13. DOI: 867673. 10.3389/fpsyg.2022.867673.
Michelaraki, Eva & Katrakazas, Christos & Kaiser, Susanne & Brijs, Tom & Yannis, George. (2023). Real-time monitoring of driver distraction: State-of-the-art and future insights. Accident Analysis & Prevention. 192. 107241. 10.1016/j.aap.2023.107241.
Road traffic injuries. Available at: https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries
Salakapuri, R., Navuri, N.K., Vobbilineni, T. et al. Integrated deep learning framework for driver distraction detection and real-time road object recognition in advanced driver assistance systems. Sci Rep 15, 25125 (2025). https://doi.org/10.1038/s41598-025-08475-4
Shagoti, Shiv & Shivakumar, & Panchal, Sujey & Patil, Rekha. (2025). Driver Drowsiness Detection System. International Journal of Latest Technology in Engineering Management & Applied Science. 14. 1026-1028. DOI: 10.51583/IJLTEMAS.2025.140500109.
Thakur, Rahul & Shivam, & Raj, Shubham & Pandey, Subhanshu. (2022). Driver Drowsiness Detection System Using Machine Learning. DOI: 10.3233/ATDE220718.
Wang, Jindong & Chen, Yiqiang & Hao, Shuji & Peng, Xiaohui & Hu, Lisha. (2017). Deep Learning for Sensor-based Activity Recognition: A Survey. Pattern Recognition Letters. 119. DOI: 10.1016/j.patrec.2018.02.010.
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Copyright (c) 2026 Сергій Іванович Отрох , Олександр Вікторович Сарафанніков , Юрій Віталійович Мельник
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