An On-board Monitoring System for Driving Fatigue and Distraction Detection

In the past few decades, it is shown in various studies that driving fatigue or distraction are the main threats of traffic accidents. Thus, the on-board monitoring for driving behaviors is becoming an important component of advanced driver assistance systems (ADAS) for intelligent vehicles. In this paper, we present the techniques to simultaneously detect the fatigue and distracted driving behaviors using vision and learning based approaches. In fatigue driving detection, we use facial features to detect the open/close of eyes, yawning and head posture. The random forest is adopted to analyze the driving conditions. In the distraction detection, the convolutional neural network (CNN) is used to classify various distracted driving behaviors. The experiments are carried out on the PC and embedded hardware platform using public and our own datasets for training and testing. Compared to the previous approaches, the proposed methods provide better results in terms of accuracy and computation time.

Automated summary

This paper presents techniques for simultaneously detecting fatigue and distracted driving behaviors using vision and learning based approaches. Facial features and machine learning models are utilized to achieve this. Experimental results show that the proposed methods outperform previous approaches in terms of accuracy and computation time.