An embedded intelligence engine for driver drowsiness detection

Abstract

Motor vehicle crashes involving drowsy driving are huge in number all over the world. Many studies revealed that 10%–30% of crashes are due to drowsy driving. Fatigue has costly effects on the safety, health, and quality of life. This drowsiness of drivers can be detected using various methods, for example, algorithms based on behavioural gestures, physiological signals and vitals. Also, few of them are vehicle based. Drowsiness of drivers was detected based on steering wheel movement and lane change patterns. A pattern is derived based on slow drifting and fast corrective steering movement. A prototype that detects the drowsiness of an automobile driver using artificial intelligence techniques, precisely using open-source tools like TensorFlow Lite on a Raspberry Pi development board, is developed. The TensorFlow model is trained on images captured from the video with the help of object detection using cascade classifier. In order to have a better accuracy, an Inception v3 architecture is used in pre-training the model with the image dataset. The final model is created and trained using long short-term memory and then the final TensorFlow model is converted to TensorFlow Lite model and this Lite model is used on Raspberry Pi board to detect the drowsiness of drivers. The results are comparable with desktop-based results in the literature.