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Ensemble CNN to Detect Drowsy Driving with In-Vehicle Sensor Data.

Yongsu Jeon¹, Beomjun Kim², Yunju Baek¹

¹Department of Information Convergence Engineering, Pusan National University, Busan 46241, Korea.

Sensors (Basel, Switzerland)

|April 3, 2021

Summary

This summary is machine-generated.

This study introduces a novel method for detecting drowsy driving using vehicle sensor data. The proposed ensemble network accurately identifies both long and short-duration drowsy driving, enhancing road safety.

Keywords:

driver status monitoring drowsy driving detection ensemble CNN intelligent vehicle safety system

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Area of Science:

Road Safety Engineering
Artificial Intelligence in Transportation
Human Factors in Driving

Background:

Drowsy driving poses a significant risk to road safety, leading to accidents.
Existing drowsy driving detection methods may lack accuracy and reliability.
Developing advanced detection systems is crucial for accident prevention.

Purpose of the Study:

To develop and evaluate a new method for detecting drowsy driving using vehicle sensor data.
To categorize drowsy driving into long-duration and short-duration types.
To propose an ensemble network model for accurate detection of different drowsy driving states.

Main Methods:

Utilized vehicle sensor data from steering wheel and pedal pressure.
Developed an ensemble network model comprising Convolutional Neural Networks (CNNs).
Implemented a novel imbalanced data-handling method for efficient training.
Collected a dataset of 198.3 hours from driving simulations in diverse environments.

Main Results:

Achieved up to 94.2% accuracy in detecting drowsy driving.
Successfully differentiated between long-duration and short-duration drowsy driving.
The proposed ensemble network demonstrated effective performance on the collected dataset.

Conclusions:

The presented method offers a reliable approach to drowsy driving detection.
The ensemble network model shows promise for real-world application in enhancing driver safety.
Accurate drowsy driving detection technology can significantly reduce traffic accidents.