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By: Pranjal, Navin Kumar Tyagi, and R. Prasad.
1. Student, Marathwada Institute of technology, Bulandshahr, Uttar Pradesh, India
2. Assistant Professor, Marathwada Institute of technology, Bulandshahr, Uttar Pradesh, India
3. Assistant Professor, Hindustan College of Science & Technology, Mathura, Uttar Pradesh, India
Driver drowsiness accounts for over 18% of all traffic accidents in India, making it a major contributing factor. This study proposes a real-time, edge-based Driver Drowsiness Detection System (DDDS) that detects early fatigue signs and alerts drivers prior to accidents. The system employs a multimodal, bimodule strategy. In order to track face traits such eye closure, blink rate, yawning, head position, and gaze direction, the first module uses computer vision. In order to identify behavioral indicators of weariness, the second module examines grip patterns and steering wheel hand pressure. To increase precision and dependability, data from both modules are integrated. Low latency, privacy, and independence from internet connectivity are ensured by the local execution of all computation on an edge device. According to experimental data, the multimodal fusion solution outperforms single-modality methods by achieving 93.8% detection accuracy. It operates in real-time on an edge device to provide minimal latency and privacy. This method offers a dependable and useful way to improve traffic safety.
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Refrences:
- O. Ajayi, A. M. Kurien, K. Djouani, and L. Dieng, “A multimodal systematic review of drivers’ fatigue detection methodologies, datasets, and models,” IEEE Access, vol. 13, pp. 1–20, 2025, doi: 10.1109/ACCESS.2025.3606900.
- Giovanni, T. Suprihadi, and K. Karyono, “DROWTION: Driver drowsiness detection software using MINDWAVE,” in Proc. IAICT, Bali, Indonesia, Aug. 2014.
- Sakthi Asan @ Sankaran, N. Vasudevan, and V. Nagarajan, “Driver Drowsiness Detection using Percentage Eye Closure Method,” in 2020 International Conference on Communication and Signal Processing (ICCSP), India, July 28–30, 2020.
- M. Hidalgo Rogel, E. T. Martínez Beltrán, M. Quiles Pérez, S. López Bernal, G. Martínez Pérez, and A. Huertas Celdrán, “Studying Drowsiness Detection Performance While Driving Through Scalable Machine Learning Models Using Electroencephalography,” Cognitive Computation, vol. 16, pp. 1253–1267, 2024, doi: 10.1007/s12559-023-10233-5.
- Abbas and A. Alsheddy, “Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis,” Sensors, vol. 21, no. 1, p. 56, Dec. 2021, doi: 10.3390/s21010056.
- F. Hassan, A. F. Ibrahim, A. Gomaa, M. A. Makhlouf, and B. Hafiz, “Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach,” Scientific Reports, vol. 14, art. no. 1369, 2024.
- Fonseca and S. Ferreira, “Drowsiness detection in drivers: A systematic review of deep learning‑based models,” Applied Sciences, vol. 15, p. 9018, 2025, doi: 10.3390/app15169018.
- Abd El-Nabi, A. F. Ibrahim, E. M. El-Rabaie, O. F. Hassan, N. F. Soliman, K. F. Ramadan, and W. El-Shafai, “Driver drowsiness detection using Swin Transformer and diffusion models for robust image denoising,” IEEE Access, vol. 3561717, 2025, doi: 10.1109/ACCESS.2025.3561717.
- Albadawi, M. Takruri, and M. Awad, “A review of recent developments in driver drowsiness detection systems,” Sensors, vol. 22, no. 5, p. 2069, 2022, doi: 10.3390/s22052069.
- Kumar and R. Singh, “Multimodal Fatigue Detection for Road Safety,” Int. J. Adv. Eng. Res. Sci., vol. 6, no. 7, pp. 1–10, 2019.
- S. Patel et al., “Driver Drowsiness Detection: A Review,” IEEE Trans. Intelligent Transportation Systems, vol. 20, no. 6, pp. 2341–2352, Jun. 2019.
- Zhang et al., “Edge Computing for Real-Time Driver Monitoring,” IEEE Access, vol. 8, pp. 15342–15353, 2020.
- K. Sharma et al., “Steering Behavior-Based Fatigue Detection,” IEEE Sensors Journal, vol. 21, no. 9, pp. 11000–11009, 2021.
- S. Bajaj, N. Kumar, and R.K. Kaushal, “Comparative Study to Detect Driver Drowsiness,” in 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2021, pp. 678–683. doi: 10.1109/ICACITE51222.2021.9404761.
- U. Maheswari, R. Aluvalu, M. V. V. P. Kantipudi, K. K. Chennam, K. Kotecha, and J. R. Saini, “Driver drowsiness prediction based on multiple aspects using image processing techniques,” IEEE Access, vol. 10, pp. 54980–54990, 2022, doi: 10.1109/ACCESS.2022.3176451.
- Gianina, M. Khadafi, A. Farzan, and Z. Abidin, “Integration of A Web Mdvr Howen Vehicle Surveillance System (Vss) and An Artificial Intelligence Based in Car Camera (Icc) For Fleet Safety PT. Putra Perkasa Abadi Jobsite Adaro Indonesia”, IJSE, vol. 6, no. 1, pp. 64–71, Jan. 2026.