AI-Driven Real-Time Driver Monitoring and Intelligent Safety Intervention Using Deep Learning Models
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Abstract
Worldwide, driver weariness and distraction are major causes of traffic accidents. This study describes an AI-driven Driver Monitoring System (DMS) that detects tiredness, distraction, and risky driving behaviours in real time using computer vision, deep learning, and sensor fusion. The suggested system calculates a risk probability index by combining an infrared camera in the cabin with a steering angle sensor and optional physiological inputs. Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTMs) are combined in a multi-stage deep learning framework to analyse temporal behaviour. Real-time intervention mechanisms, such as vibration feedback, auditory alarms, and simulated braking control, are demonstrated in a hardware simulation prototype that uses embedded edge devices. Advanced Driver Assistance Systems (ADAS) can locate objects with high accuracy and minimal latency, according to experimental testing [1].
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References
S. Mudassar Shah et al., “AI-enabled driver assistance: Monitoring head and gaze movements for enhanced safety,” Complex & Intelligent Systems, vol. 11, Art. no. 297, May 2025.
N. Chaitanya Kumar et al., “A real-time driver drowsiness detection and vehicle control system with GPS,” in Proc. IEEE Int. Conf. Computer Systems and Communication Engineering (ICCSCE), Nov. 2025.DOI: https://doi.org/10.2991/978-94-6463-858-5_283
Y. V. Bhatt and N. A. Joshi, “State-of-the-art in driver’s drowsiness detection: A comprehensive survey,” Journal of Information Systems Engineering and Management, vol. 10, no. 36s, 2025.
https://jisem-journal.com/index.php/journal/article/view/6630
P. Kavya et al., “A vision-based driver drowsiness detection framework for road safety enhancement,” in Proc. Int. Conf. Intelligent Technologies in Smart Machines (ICITSM), Oct. 2025.
https://eudl.eu/doi/10.4108/eai.28-4-2025.2357762
J. Jyoti, N. Kumar, and Kunal, “Real-time driver drowsiness detection with machine learning,” in Proc. Int. Conf. Advances in Artificial Intelligence (ICAAAI), June 2025.DOI: https://doi.org/10.2991/978-94-6463-738-0_60
Y. Pathak et al., “Real-time anti-sleep alert algorithm to prevent road accidents,” Frontiers in Future Transportation, vol. 6, Art. no. 1545411, Mar. 2025.
https://www.frontiersin.org/journals/future-transportation/articles/10.3389/ffutr.2025.1545411/full
S. Abhay et al., “A hybrid learning approach for real-time distracted driver behaviour detection using transfer learning,” International Journal of Innovative Science and Research Technology, Nov. 2025. DOI: https://doi.org/10.38124/ijisrt/25nov1132
S. Brunda et al., “Driver assistance system: Utilizing machine learning for reducing accidents,” International Advanced Research Journal in Science, Engineering and Technology (IARJSET), vol. 12, no. 5, May 2025.
https://iarjset.com/wp-content/uploads/2025/05/IARJSET.2025.12509.pdf
“Drive safe emotive transport initiative using AI,” Journal of Emerging Technologies and Innovative Research (JETIR), vol. 12, no. 7, 2025.https://www.jetir.org/papers/JETIRGX06070.pdf
“Neuro-cognitive AI-driven system for preventing road accidents through driver drowsiness alerts: A hybrid CNN–GRU framework,” ScienceDirect, 2025. DOI: https://doi.org/10.1016/j.array.2025.100631
Y. Dinesh et al., “AI-driven accident minimisation and human safety enhancement in transport systems,” in Proc. Int. Conf. Intelligent Technologies in Smart Machines (ICITSM), Oct. 2025.(https://eudl.eu/doi/10.4108/eai.28-4-2025.235808).
S. Chaikhamwang et al., “Driver drowsiness detection and alert system using Internet of Things (IoT) technology,” International Journal of IoT and Data Science, vol. 3, no. 1, 2025. https://iaeme.com/MasterAdmin/Journal_uploads/IJIDS/VOLUME_3_ISSUE_1/IJIDS_03_01_001.pdf
A. Rehman et al., “Identification, detection and YOLOv5-based driver drowsiness framework,” International Journal of Innovative Science and Research Technology, May 2025. DOI: https://doi.org/10.38124/ijisrt/25apr1476
“Enhancing driver safety: Real-time distraction and drowsiness detection using deep learning,” Indian Journal of Computer Science, 2024–2025. https://www.indianjournaloffinance.co.in/index.php/tcsj/article/view/174565
“AI-enabled enforcement system to reduce road crash risk by influencing driver behaviour,” Case
Studies on Transport Policy, 2025.