ABSTRACT
Drowsiness is a common problem that many drivers encounter due to long working hours, lack of sleep, and tiredness. Tired drivers are as dangerous as drunk drivers because they have slower reaction times and suffer from reduced attention, awareness, and ability to control their vehicles. Drowsy driving causes many traffic accidents, especially fatal crashes. Therefore, the best way to prevent accidents involving drowsiness is to alert the drivers ahead of time. The accuracy of the drowsiness prediction reduces if the studies only focus on facial landmarks, ignoring other fatigue features such as tilting head, blinking, and yawning. To solve these problems, we propose an approach to detect driver drowsiness efficiently and accurately using IoT and deep neural networks improved from LSTM, VGG16, InceptionV3, and DenseNet. The use of transfer learning technique combined with multiple drowsiness signs is to improve the accuracy of the drowsiness detection in various driving conditions. The time-varying factor is also taken into consideration in the models developed from LSTM and DenseNet. When the driver's fatigue is detected, the IoT module emits a warning message along with a sound through a Jetson Nano monitoring system. The experimental results demonstrate that our approach using deep neural networks can achieve high accuracy of up to 98%. Notably, this approach has also been verified in cases with/without wearing a mask and glasses. This has a practical meaning in the Covid-19 pandemic situation when everyone needs to comply with the wearing of masks in public places.