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Real-time driver activity detection using advanced deep learning models.

Md Al Emran1, Md Ariful Islam1, Md Obaydullahn Khan1

  • 1Pabna University of Science and Technology, Pabna, Bangladesh.

Cognitive Neurodynamics
|November 17, 2025
PubMed
Summary

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This summary is machine-generated.

A new AI model accurately detects driver behaviors like distraction and drowsiness to enhance road safety. This advanced system shows high accuracy in real-time applications, aiming to reduce traffic accidents globally.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traffic accidents are a major global safety concern, often caused by driver inattention, sleepiness, and distraction.
  • Computer vision and AI offer promising solutions for real-time driver monitoring systems to mitigate these risks.

Purpose of the Study:

  • To develop and evaluate a novel deep learning architecture for multi-class driver activity categorization.
  • To compare the proposed model's performance against established deep learning models for driver behavior analysis.

Main Methods:

  • Assessed four deep learning models: MobileNetV2, DenseNet201, NASNetMobile, and VGG19.
  • Proposed a unique Hybrid CNN-Transformer architecture enhanced with Efficient Channel Attention (ECA).
  • The framework categorizes seven key driving behaviors: Closed Eye, Open Eye, Dangerous Driving, Distracted Driving, Drinking, Yawning, and Safe Driving.
Keywords:
Distracted DrivingDriver behavior classificationEfficient Channel AttentionHybrid CNN-TransformerIntelligent transportation systemsTransfer learning

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Main Results:

  • Baseline models DenseNet201 and MobileNetV2 achieved high validation accuracies (99.40% and 99.31%, respectively).
  • The proposed Hybrid CNN-Transformer with ECA achieved a near-perfect validation accuracy of 99.72%.
  • The model demonstrated flawless generalization, achieving 100% accuracy on an independent test set, with minimal misclassifications.

Conclusions:

  • The Hybrid CNN-Transformer with ECA effectively merges CNN local feature extraction, attention-based refinement, and Transformer global context modeling for robust and efficient driver monitoring.
  • The developed system shows significant potential for real-time intelligent transportation applications, contributing to reduced traffic accidents and improved road safety.