DOI: https://doi.org/10.32515/2664-262X.2023.7(38).2.139-146
Development of an Algorithm for Monitoring the Driver's Condition Using an Android Application in Order to Increase the Level of Active Safety
About the Authors
Alla Yovchenko, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Cherkassy State Technological University, Cherkassy, Ukraine, e-mail: a.yovchenko@chdtu.edu.ua, ORCID ID: 0000-0002-7069-1092
Ihor Shlionchak, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Cherkassy State Technological University, Cherkassy, Ukraine, e-mail: Igor_Shlionchak@ukr.net
Abstract
The purpose of the research is the analysis of existing systems for monitoring the driver's condition using an Android mobile application to increase the level of active safety with the development of an algorithm for monitoring the driver's condition. At the same time, such parameters as turns and tilts of the head, duration of eyelid blinking, yawning are taken into account. As a result, the state of drowsiness, impaired attention, and drunkenness of the driver is analyzed.
The article analyzes the methods of monitoring the dangerous condition of the driver during the movement of the vehicle. The list of programs used in this case is given. The Drive Safely mobile application was chosen for the research, which is based on monitoring the condition of the driver's eyes and mouth. Based on research, eye PERCLOS should not exceed 28% and mouth PERCLOS should not exceed 30%. An algorithm for recognizing emergency situations in the vehicle cabin based on the image of the driver's eyes and mouth from the front camera of a smartphone has been developed. As a result of the analysis of existing Android applications, an algorithm for recognizing emergency situations in the cabin of the vehicle was developed. The algorithm was obtained based on the image of the smartphone's front camera. This algorithm takes into account all possible dangerous conditions of the driver behind the wheel of the vehicle.
As a result of the conducted research, an analysis of the existing systems for monitoring the driver's condition using the Drive Safely mobile Android application was carried out. As a result of the analysis of existing Android applications, an algorithm for recognizing emergency situations in the cabin of the vehicle was developed. The use of this algorithm in the program and the connection of its results to the cloud environment will allow it to self-improve and increase the efficiency of its operation, thus increasing the level of active security.
Keywords
monitoring, dangerous condition, driver, active safety, emergency situations, smartphone, vehicle
Full Text:
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References
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Citations
- Driver fatigue and road accidents. The Royal Society for the Prevention of Accidents, 2011. 4 p.
- Akerstedt T., Gillberg M. Subjective and objective sleepiness in the active individual . Int. Journal Neurosci. 2014. Vol. 52. р. 29-37
- Driver Identification Using Driving Behavior Signals / T. Wakita et al. Proceedings of the IEEE Intelligent Transportation Systems, 2005. 396-401p.
- Гюлев, Н. У., Доля В. К., Доля О. В. Экспериментальное определение транспортного утомления пассажиров при поездке на работу. Київ: УкрНИИНТИ 18.06.90 г., № 1136.Ук90
- Jiadi Yu, Yingying Chen, Xiangyu Xu. Sensing Vehicle Conditions for Detecting Driving Behaviors, 2018. 81 p.
- Distracted Driving:Traffic Safety Facts Research Note. Report No. DOT HS 812 517, Washington, DC: National Highway Traffic Safety Administration. April, 2018. 6 p.
- Global status report on road safety 2022. Geneva: World Health Organization; 2022. URL: https://apps.who.int/iris/ bitstream/handle/ 10665/277370/WHO-NMH-NVI-18.20-eng.pdf.
- Fatigue and crash risk URL: https://ec.europa.eu/transport/road_safety/specialist/knowledge/fatique/ fatigue_and_road_crashes/fatigue_and_crash_risk_en.
- W. Sakpere, M. Adeyeye-Oshin, N. Mlitwa. A state-of-the-art survey of indoor positioning and navigation systems and technologies. South African Computer Journal. №29. 2017. 145-197p.
- iOnRoad. URL: http://www.ionroad.com (дата звернення: 17.03.2023).
- Augmented Driving. URL: http://www.imaginyze.com/ (дата звернення: 20.03.2023)
- Driver Guard, URL: https://play.google.com/store/apps/details?id=com.badrit.cv. vehicledetect (дата звернення: 20.03.2023).
- Nexar – AI Dashcam. URL: https://www.getnexar.com/ (дата звернення: 20.03.2023)
- NightDrive.URL: https://itunes.apple.com/us/app/nightdrive/id902703316?mt=8 (дата звернення: 20.03.2023).
- Dinges, D. PERCLOS: A Valid Psychophysiology Measure of Alertness as Assessed by Psychomotor Vigilance, Technical Report Federal Highway Administration: Washington, DC, USA, 2018
- Viola P., Jones M. Rapid Object Detection using a Boosted Cascade of Simple Features Mitsubishi Electr. Res. Labs. Cambridge, MA, USA, 2014.
- Open CV library, URL: https://opencv.org/ (дата звернення 20.03.2023).
- Метод Виолы-Джонса как основа для распознавания лиц, URL: https://habrahabr.ru/post/133826/.
- Soukupova Т, Cech J. Real-Time Eye Blink Detection using Facial Landmarks, Center for Machine Perception, Department of Cybernetics Faculty of Electrical Engineering, Czech Technical University in Prague, 2016.
- Towards Data Science, 2016. URL: https:// towardsdatascience.com/ (дата звернення 20.03.2023).
- Eriksson, M. Eye-tracking for detection of driver fatigue. Papanikolopoulos, Proceedings of the IEEE Conference on Intelligent Transportation Systems (ITSC), 2017. 314-319p.
Copyright (c) 2023 Alla Yovchenko, Ihor Shlionchak
Development of an Algorithm for Monitoring the Driver's Condition Using an Android Application in Order to Increase the Level of Active Safety
About the Authors
Alla Yovchenko, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Cherkassy State Technological University, Cherkassy, Ukraine, e-mail: a.yovchenko@chdtu.edu.ua, ORCID ID: 0000-0002-7069-1092
Ihor Shlionchak, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Cherkassy State Technological University, Cherkassy, Ukraine, e-mail: Igor_Shlionchak@ukr.net
Abstract
Keywords
Full Text:
PDFReferences
1. Driver fatigue and road accidents (2011). The Royal Society for the Prevention of Accidents [in English].
2. Akerstedt, T. (2014) . Subjective and objective sleepiness in the active individual. Int. J. Neurosci. Vol. 52. 29-37 [in English].
3. Driver Identification Using Driving Behavior Signals (2015). Proceedings of the IEEE Intelligent Transportation Systems. 396-401 [in English].
4. Gjulev, N.U., Dolja V.K., & Dolja, O.V. Jeksperimental'noe opredelenie transportnogo utomlenija passazhirov pri poezdke na rabotu. Kiїv: UkrNIINTI 18.06.90 g., № 1136.Uk90 [in Russian].
5. Jiadi, Yu, Yingying, Chen & Xiangyu, Xu (2018). Sensing Vehicle Conditions for Detecting Driving Behaviors [in English].
6. Distracted Driving: Traffic Safety Facts Research Note (2018). Report No. DOT HS 812 517, Washington, DC: National Highway Traffic Safety Administration. April, 6 [in English].
7. Global status report on road safety (2023). Geneva: World Health Organization. apps.who.int . Retrieved from https://apps.who.int/iris/ bitstream/handle/ 10665/277370/WHO-NMH-NVI-18.20-eng.pdf. [in English].
8. Fatigue and crash risk (2015). ec.europa.eu , Retrieved from https://ec.europa.eu/transport/road_safety/ specialist/knowledge/fatique/fatigue_and_road_crashes/fatigue_and_crash_risk_en [in English].
9. W. Sakpere, M. Adeyeye-Oshin & N. Mlitwa (2017). A state-of-the-art survey of indoor positioning and navigation systems and technologies. South African Computer Journal. №29. 145-197 [in English].
10. iOnRoad (2023). ionroad.com. Retrieved from http://www.ionroad.com [in English].
11. Augmented Driving (2023). Retrieved from http://www.imaginyze.com/ [in English].
12. Driver Guard (2023). play.google.com . Retrieved from https://play.google.com/store/apps/details?id=com.badrit.cv. vehicledetect [in English].
13. Nexar – AI Dashcam. (2023). getnexar.com. Retrieved from https://www.getnexar.com/ [in English].
14. NightDrive (2023). itunes.apple.com. Retrieved from https://itunes.apple.com/us/app/ nightdrive/id902703316?mt=8 [in English].
15. Dinges, D. (2018). PERCLOS: A Valid Psychophysiology Measure of Alertness as Assessed by Psychomotor Vigilance. Technical Report Federal Highway Administration: Washington, DC, USA [in English].
16. Viola, P. & Jones, M. (2014). Rapid Object Detection using a Boosted Cascade of Simple Features, Mitsubishi Electr. Res. Labs. Cambridge, MA, USA [in English].
17. Open CV library (2023). opencv.org. Retrieved from https://opencv.org/ [in English].
18. Metod Violy-Dzhonsa kak osnova dlja raspoznavanija lic [Viola-Jones method as a basis for face recognition]. (2023). habrahabr.ru. Retrieved from https://habrahabr.ru/post/133826/ [in Russian].
19. Soukupova, Т. & Cech, J. (2016). Real-Time Eye Blink Detection using Facial Landmarks. Center for Machine Perception, Department of Cybernetics Faculty of Electrical Engineering, Czech Technical University in Prague [in English].
20. Towards Data Science. (2016). towardsdatascience.com. Retrieved from URL: https://towardsdatascience.com/ [in English].
21. Eriksson, M. (2017). Eye-tracking for detection of driver fatigue. Papanikolopoulos, Proceedings of the IEEE Conference on Intelligent Transportation Systems (ITSC), 314-319 [in English].
Citations
- Driver fatigue and road accidents. The Royal Society for the Prevention of Accidents, 2011. 4 p.
- Akerstedt T., Gillberg M. Subjective and objective sleepiness in the active individual . Int. Journal Neurosci. 2014. Vol. 52. р. 29-37
- Driver Identification Using Driving Behavior Signals / T. Wakita et al. Proceedings of the IEEE Intelligent Transportation Systems, 2005. 396-401p.
- Гюлев, Н. У., Доля В. К., Доля О. В. Экспериментальное определение транспортного утомления пассажиров при поездке на работу. Київ: УкрНИИНТИ 18.06.90 г., № 1136.Ук90
- Jiadi Yu, Yingying Chen, Xiangyu Xu. Sensing Vehicle Conditions for Detecting Driving Behaviors, 2018. 81 p.
- Distracted Driving:Traffic Safety Facts Research Note. Report No. DOT HS 812 517, Washington, DC: National Highway Traffic Safety Administration. April, 2018. 6 p.
- Global status report on road safety 2022. Geneva: World Health Organization; 2022. URL: https://apps.who.int/iris/ bitstream/handle/ 10665/277370/WHO-NMH-NVI-18.20-eng.pdf.
- Fatigue and crash risk URL: https://ec.europa.eu/transport/road_safety/specialist/knowledge/fatique/ fatigue_and_road_crashes/fatigue_and_crash_risk_en.
- W. Sakpere, M. Adeyeye-Oshin, N. Mlitwa. A state-of-the-art survey of indoor positioning and navigation systems and technologies. South African Computer Journal. №29. 2017. 145-197p.
- iOnRoad. URL: http://www.ionroad.com (дата звернення: 17.03.2023).
- Augmented Driving. URL: http://www.imaginyze.com/ (дата звернення: 20.03.2023)
- Driver Guard, URL: https://play.google.com/store/apps/details?id=com.badrit.cv. vehicledetect (дата звернення: 20.03.2023).
- Nexar – AI Dashcam. URL: https://www.getnexar.com/ (дата звернення: 20.03.2023)
- NightDrive.URL: https://itunes.apple.com/us/app/nightdrive/id902703316?mt=8 (дата звернення: 20.03.2023).
- Dinges, D. PERCLOS: A Valid Psychophysiology Measure of Alertness as Assessed by Psychomotor Vigilance, Technical Report Federal Highway Administration: Washington, DC, USA, 2018
- Viola P., Jones M. Rapid Object Detection using a Boosted Cascade of Simple Features Mitsubishi Electr. Res. Labs. Cambridge, MA, USA, 2014.
- Open CV library, URL: https://opencv.org/ (дата звернення 20.03.2023).
- Метод Виолы-Джонса как основа для распознавания лиц, URL: https://habrahabr.ru/post/133826/.
- Soukupova Т, Cech J. Real-Time Eye Blink Detection using Facial Landmarks, Center for Machine Perception, Department of Cybernetics Faculty of Electrical Engineering, Czech Technical University in Prague, 2016.
- Towards Data Science, 2016. URL: https:// towardsdatascience.com/ (дата звернення 20.03.2023).
- Eriksson, M. Eye-tracking for detection of driver fatigue. Papanikolopoulos, Proceedings of the IEEE Conference on Intelligent Transportation Systems (ITSC), 2017. 314-319p.