Analysis of Driver Monitoring System Tool Needs to Reduce the Risk of Traffic Accidents for Maxim Drivers in Mendalo Darat
DOI:
https://doi.org/10.31884/random.v3i2.62Keywords:
Driver Monitoring System, Traffic accidents, Driver fatigue , Transportation safetyAbstract
Traffic accidents caused by human factors, particularly drivers, are a serious and growing issue. This research aims to analyze the need for a Driver Monitoring System (DMS) as a solution to reduce traffic accident risks in Mendalo Darat Village, specifically among Maxim drivers. The research methodology involves surveys, structured interviews with Maxim drivers, and statistical analysis of traffic accident data in the area. Findings indicate that drivers often experience fatigue, concentration issues, and lack awareness of their physical condition while driving. The proposed DMS will be designed to detect signs of driver fatigue and distraction, providing early warnings to mitigate accident risks. This study is expected to contribute to enhancing traffic safety and driver welfare in Mendalo Darat Village.
Downloads
References
R. Grace, V. E. Byme, D. M. Bierman, J. M. Legrand, D. Gricourt, R. K. Davis, J. J. Staszewski and B. Carnahan, “A Drowsy Driver Detection System for Heavy Vehicles”, Proceedings of 17th AIAA/IEEE/SAE Digital Avionics Systems Conference (DASC), (1998) November, Washington, USA.
H. Veeraraghavan and N. Papanikolopoulos, “Detecting Driver Fatigue Through the Use of Advanced Face Monitoring Techniques”, Intelligent Transportation System Institute, Department of Computer Science and Engineering, University of Minnesota, (2001).
F. Y. Luthfia, “Mendeteksi Kantuk Pada Pengemudi Mobil Menggunakan Metode HaarCascade,” Universitas Muhammadiyah Malang, 2022
C. K. U. Nggiku, A. Rabi, and S. Subairi, “Deteksi Kantuk Untuk Keamanan Berkendara Berbasis Pengolahan Citra,” J.JEETech, vol. 4, no. 1, pp. 48–56, 2023
G. De-Las-Heras, J. Sánchez-Soriano, and E. Puertas, “Advanced driver assistance systems (ADAS) based on machine learning techniques for the detection and transcription of variable message signs on roads,” Sensors, vol. 21, no. 17, pp. 1–18, 2021, doi:10.3390/s21175866.
L. Wang et al., “Advanced Driver-Assistance System (ADAS) for Intelligent Transportation Based on the Recognition of Traffic Cones,” Adv. Civ. Eng., vol. 2020, 2020, doi: 10.1155/2020/8883639.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Journal of Informatics and Computing (RANDOM)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.