Analysis of Driver Fatigue Eye Movement Characteristics Under Intelligent Supervision Environment

Reducing drivers' improper driving behavior in transportation is an important condition for ensuring safety. In today's intelligent era, many passenger and freight companies are gradually using intelligent monitoring platforms to supervise the behavior and condition of drivers and vehicles. The intelligent supervision platform is beneficial for fatigue monitoring and improving driving safety. However, the research found that the number of alarms on the platform is too high, and the smart regulatory environment exhibits different operational characteristics compared to traditional driving environments. Therefore, based on the characteristics of driving behavior in the context of smart regulatory information interaction. It is very necessary to explore the influencing factors and development patterns of driving fatigue under the intelligent supervision environment. This article uses principal component analysis to identify sensitive indicators of driving fatigue eye movement characteristics in the context of intelligent supervision platforms, with the sensitive indicators being pupil diameter and blink frequency. This study helps to reveal the formation and variation rules of driver fatigue under the environmental conditions of intelligent supervision platforms, improving the accuracy and reliability of fatigue detection while driving.