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Data fusion to develop a driver drowsiness detection system with robustness to signal loss
Sajjad Samiee1, Shahram Azadi2, Reza Kazemi3
1Khaje Nasir Toosi University of Technology, Faculty of Mechanical Engineering, Tehran 19991-43344, Iran. S.Samiee@tugraz.at.
Sensors (Basel, Switzerland)
|September 27, 2014
Summary
This study introduces a robust drowsiness detection system combining multiple methods for reliable driver monitoring. The system ensures continuous operation even with signal loss, enhancing road safety.
Area of Science:
- * Human-computer interaction
- * Artificial intelligence in transportation
Background:
- * Drowsiness is a major cause of road accidents.
- * Existing drowsiness detection systems can be unreliable due to signal loss or intrusive methods.
Purpose of the Study:
- * To develop a robust and reliable drowsiness detection system.
- * To combine image processing and driver-vehicle interaction methods.
- * To ensure non-intrusive monitoring to avoid driver distraction.
Main Methods:
- * A hybrid approach combining image processing and driver-vehicle interaction.
- * Utilized artificial neural networks for system design.
- * Data collected using a driving simulator with controlled volunteer sleep patterns.
Main Results:
- * The system demonstrated high reliability in drowsiness detection.
- * The system exhibited robustness against input signal loss.
- * Accuracy improvement was not significant, but reliability and robustness were key advantages.
Conclusions:
- * The proposed system enhances the reliability of drowsiness detection.
- * Robustness to signal loss ensures continuous driver monitoring.
- * Reliable drowsiness detection systems are crucial for reducing road accidents and associated costs.
