THE APPLICATION OF DRIVING FATIGUE DETECTION AND MONITORING TECHNOLOGIES IN TRANSPORTATION SECTOR: A REVIEW
Abstract
Driving fatigue is the leading cause of traffic accidents in many countries, prompting the development of a number of fatigue detection devices. This paper concisely reviews the existing fatigue detection system for transportation sectors. A rigorous systematic literature review (SLR) was utilized to find robust and high-potential material related to the research issue. According to the available literature research, many fatigue detection devices have been developed and commercialized, categorized into three groups based on the detection target's features: vehicle-based parameters, behaviour-based parameters and physiological-based parameters. However, currently available driver fatigue detection systems are divided into two categories: (i) very expensive systems that are limited to specific high-end automobile models and (ii) affordable alternatives for old and cheap vehicles that are not robust. Regardless of the physiological-based parameters' great accuracy in identifying driving fatigue, practically all available fatigue detection devices are classified as vehicle and driver behaviour-based parameters. As a result, this study looked into the use of physiological method in the future fatigue detection studies. The study's findings will help researchers, politicians, and practitioners create a system to significantly reduce road accidents and improve road safety.
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Anderson, C., Chang, A., Ronda, J., & Czeisler, C. (2010). Real-time drowsiness as determined by infrareflectance oculography is commensurate with gold standard laboratory measures: a validation study. Sleep,
Benz, M. (2023). Mercedes-Benz ATTENTION ASSIST Explained. https://www.mercedesbenzofeaston.com/mercedes-benz-attention-assist/
Caprioli, J., & Coleman, A. L. (2010). Blood pressure, perfusion pressure, and glaucoma. American journal of ophthalmology, 149(5), 704-712.
CEIC. (2021). Malaysia Number of Registered Vehicles. https://www.ceicdata.com/en/ indicator/malaysia/number-of-registered-vehicles#: ~:text=Malaysia%20Number%20of%20Registered%20Vehicles%20was%20reported%20at%2017%2C486%2C589%20Unit,17%2C283%2C951%20Unit%20for%20Sep%202020.
Dewey, A. D., A. (2016). Introduction to systematic reviews: online learning module Cochrane Training https://training.cochrane.org/interactivelearning/module-1-introduction-conducting-systematic-reviews
Dong, N., Li, Y., Gao, Z., Ip, W. H., & Yung, K. L. (2019). A WPCA-based method for detecting fatigue driving from EEG-based internet of vehicles system. IEEE Access, 7, 124702-124711.
Du, G., Li, T., Li, C., Liu, P. X., & Li, D. (2020). Vision-based fatigue driving recognition method integrating heart rate and facial features. IEEE transactions on intelligent transportation systems, 22(5), 3089-3100.
Eye, S. (2023). Intelligent safety features detecting driver state and behavior. https://smarteye.se/
FMI. (2022). PREDICTIVE AND NON-INVASIVE MONITORING. https://fmiapplications.com/astid-splash-page.html
Ford. (2023). What is my Ford's Driver Alert System*? https://www.ford.com/support/how-tos/ford-technology/driver-assist-features/what-is-the-driver-alert-system/
Geobox. (2019). DFM-HV PRO Driver Fatigue Monitor. https://geobox.com.au/product/ driver-fatigue-monitor-truck-heavy-vehicles-dfm-hv-pro/
Ghourabi, A., Ghazouani, H., & Barhoumi, W. (2020). Driver drowsiness detection based on joint monitoring of yawning, blinking and nodding. 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP),
Ibrahim, M. S., Kamat, S. R., Shamsuddin, S., & Md Isa, M. H. (2022, 2022-03-19). DEVELOPMENT OF DRIVING FATIGUE STRAIN INDEX USING FUZZY LOGIC TO QUANTIFY IMPAIRMENT RISK LEVELS OF COGNITIVE SKILLS IN VEHICLE DRIVING. International Journal of Engineering Advanced Research (1), 121-139%V 124. https://myjms.mohe.gov.my/index.php/ijear/article/view/17712
Jiang, K., Ling, F., Feng, Z., Wang, K., & Shao, C. (2017). Why do drivers continue driving while fatigued? An application of the theory of planned behaviour. Transportation Research Part A: Policy and Practice, 98, 141-149.
Jie, Z., Mahmoud, M., Stafford-Fraser, Q., Robinson, P., Dias, E., & Skrypchuk, L. (2018). Analysis of yawning behaviour in spontaneous expressions of drowsy drivers. 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018),
Junaedi, S., & Akbar, H. (2018). Driver drowsiness detection based on face feature and PERCLOS. Journal of Physics: Conference Series,
Lenné, M. G., & Fitzharris, M. (2016). Real-time feedback reduces the incidence of fatigue events in heavy vehicle fleets. Proceedings of the 23rd ITS World Congress, Melbourne, Australia,
Li, K., Gong, Y., & Ren, Z. (2020). A fatigue driving detection algorithm based on facial multi-feature fusion. IEEE Access, 8, 101244-101259.
Machines, S. (2023). Guardian driver fatigue and distraction solution. https://seeingmachines.com/products/fleet/
MacLean, A. W., Davies, D. R. T., & Thiele, K. (2003, 2003/01/01/). The hazards and prevention of driving while sleepy. Sleep Medicine Reviews, 7(6), 507-521. https://doi.org/https://doi.org/10.1016/S1087-0792(03)90004-9
Manikandan, P., Reddy, M. R. S., Mehatab, S., & Sai, P. M. (2021). Automobile Drivers Distraction Avoiding System Using Galvanic Skin Responses. 2021 6th International Conference on Communication and Electronics Systems (ICCES),
Mehta, S., Dadhich, S., Gumber, S., & Jadhav Bhatt, A. (2019). Real-time driver drowsiness detection system using eye aspect ratio and eye closure ratio. Proceedings of international conference on sustainable computing in science, technology and management (SUSCOM), Amity University Rajasthan, Jaipur-India,
O'Neill, A. (2022). Malaysia: Urbanization from 2010 to 2020 https://www.statista.com/ statistics/455880/urbanization-in-malaysia/#:~:text=Malaysia%20is%20currently% 20one%20of,to%2074%20percent%20in%202014.
Optalert. (2023). The gold standard in drowsiness detection. https://www.optalert.com/ solutions/automotive/
Peng, Z., Rong, J., Wu, Y., Zhou, C., Yuan, Y., & Shao, X. (2021). Exploring the different patterns for generation process of driving fatigue based on individual driving behavior parameters. Transportation research record, 2675(8), 408-421.
Peppes, N., Alexakis, T., Adamopoulou, E., & Demestichas, K. (2021). Driving behaviour analysis using machine and deep learning methods for continuous streams of vehicular data. Sensors, 21(14), 4704.
Rajaratnam, S., & Howard, M. (2011). Evaluation of the SmartCap technology to monitor drowsiness in healthy volunteers exposed to sleep restriction–Relationship between the SmartCap fatigue algorithm and frequent misses on the Osler Test.
Rampichini, S., Vieira, T. M., Castiglioni, P., & Merati, G. (2020). Complexity analysis of surface electromyography for assessing the myoelectric manifestation of muscle fatigue: A review. Entropy, 22(5), 529.
Ramzan, M., Khan, H. U., Awan, S. M., Ismail, A., Ilyas, M., & Mahmood, A. (2019). A survey on state-of-the-art drowsiness detection techniques. IEEE Access, 7, 61904-61919.
SmartCap. (2023). Where fatigue is the problem, SmartCap is the solution. https://www.smartcaptech.com/life-smart-cap/
Toyota. (2005). Toyota Enhances Pre-crash Safety System With Driver-monitoring Function. https://global.toyota/en/detail/248128
Tuncer, T., Dogan, S., Ertam, F., & Subasi, A. (2021). A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals. Cognitive neurodynamics, 15(2), 223-237.
Volkswagen. (2023). Driver Alert System. https://www.volkswagen.co.uk/en/ technology/car-safety/driver-alert-system.html
Volvo. (2018). Lane Departure Warning (LDW). https://www.volvocars.com/en-th/support/manuals/v60/2014w20/driver-support/driver-alert-system/lane-departure-warning-ldw
Volvo. (2020). Driver Alert Control. https://www.volvocars.com/lb/support/car/xc60/ 2020w46/article/41b16f26897fe720c0a8015138d5d35c
Yang, C., Wang, X., & Mao, S. (2020). Respiration monitoring with RFID in driving environments. IEEE Journal on Selected Areas in Communications, 39(2), 500-512.
Zhou, F., Alsaid, A., Blommer, M., Curry, R., Swaminathan, R., Kochhar, D., Talamonti, W., Tijerina, L., & Lei, B. (2020). Driver fatigue transition prediction in highly automated driving using physiological features. Expert Systems with Applications, 147, 113204.
Published
2023-06-07
How to Cite
IBRAHIM, Muhammad Shafiq; KAMAT, Seri Rahayu; SHAMSUDDIN, Syamimi.
THE APPLICATION OF DRIVING FATIGUE DETECTION AND MONITORING TECHNOLOGIES IN TRANSPORTATION SECTOR: A REVIEW.
International Journal of Technology Management and Information System, [S.l.], v. 5, n. 2, p. 30-42, june 2023.
ISSN 2710-6268.
Available at: <https://myjms.mohe.gov.my/index.php/ijtmis/article/view/22629>. Date accessed: 15 dec. 2024.
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