Published March 30, 2026 | Version CC-BY-NC-ND 4.0
Journal article Open

AI-Driven Real-Time Driver Monitoring and Intelligent Safety Intervention Using Deep Learning Models

Authors/Creators

  • 1. Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur(D) (Andhra Pradesh), India.

Contributors

Contact person:

  • 1. Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur(D) (Andhra Pradesh), India.
  • 2. Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur(D) (Andhra Pradesh), India.
  • 3. Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur(D) (Andhra Pradesh), India.

Description

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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Additional details

Identifiers

DOI
10.35940/ijese.D2642.14040326
EISSN
2319-6378

Dates

Accepted
2026-03-15
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

References

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