Driver's hazardous state detection in human-computer interaction of automotive cockpits

Abstract

Today, the smart car industry is growing rapidly, the functions of the intelligent cockpit based on human-computer interaction are more and more extensive, and the sales volume of intelligent vehicles continues to rise. The incidence of traffic crashes caused by the unsafe state of drivers remains high. The different behavioral states that drivers may emit during driving is a necessary consideration in the design of the intelligent cockpit. This paper takes the driver's state as the starting point to systematically consider the driver's state detection. Summarizing the driver's state detection from four parts: eye state, limb state, facial state, and language state. This paper introduces the development status of the current four types of detection systems, focusing on eye state recognition and limb state recognition. The key driver's characteristic signals are mainly collected by the camera. The driver's state is judged by deep learning, machine learning, and database. This paper is more systematic and comprehensive than the existing literature. Comprehensive consideration of the driver's state contributes to the driver and passengers.