AI-Driven Real-Time Driver Monitoring and Intelligent Safety Intervention Using Deep Learning Models
Yateesh Gutti1, D. Vishnu Vardhan2, H Mahaboob Peer3, B. Vijayendra Reddy4
1Yateesh Gutti, Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur(D) (Andhra Pradesh), India.
2Dr. D. Vishnu Vardhan, Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur(D) (Andhra Pradesh), India.
3H Mahaboob Peer, Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur(D) (Andhra Pradesh),India.
4Vijayendra Reddy, Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur(D) (Andhra Pradesh), India.
Manuscript received on 01 March 2026 | Revised Manuscript received on 06 March 2026 | Manuscript Accepted on 15 March 2026 | Manuscript published on 30 March 2026 | PP: 37-42 | Volume-14 Issue-4, March 2026 | Retrieval Number: 100.1/ijese.D264214040426 | DOI: 10.35940/ijese.D2642.14040326
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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].
Keywords: Driver Monitoring System, Deep Learning, CNN, LSTM, Drowsiness Detection, ADAS, Edge AI.
Scope of the Article: Computer Science and Engineering