Data fusion for driver drowsiness recognition: A multimodal perspective

Abstract

Drowsiness is characterized by decreased alertness and an increased inclination to fall asleep, typically from factors such as fatigue, sleep deprivation, or other related influences. In the context of driving, drowsiness poses substantial safety risks. The detection of driver drowsiness is of paramount importance in ensuring road safety, contributing to a significant number of accidents worldwide. Utilizing AI for drowsiness detection offers a potent solution to enhance road safety by identifying driver fatigue and preventing potential accidents. The proposed system addresses the challenge of detecting driver drowsiness using a WACHSens dataset collected from both manual and automated driving modes, encompassing rested and fatigued states. Various data sources, including vehicle-related information, facial expressions, and bio signals are employed to create a robust drowsiness detection system. A novel approach that leverages Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to effectively detect drowsiness in drivers and achieve a 96 % accuracy level. It helps in enhancing road safety by devising effective drowsiness detection mechanisms, potentially preventing accidents and saving lives. Recall, accuracy, f1-score, and precision are the performance metrics to measure the drowsiness condition.

Keywords

• Drowsiness
• Convolutional Neural Networks
• Long Short-Term Memory
• Manual Mode
• Automated Mode
• Rested