Research on Visual Distraction Characteristics in Intelligent Connected Vehicles

With the rapid development of information and network technologies, intelligent connectivity has become a dominant trend in the automotive industry. While providing a richer driving experience for its users, intelligent connected vehicles bring about more complex challenges to driving safety. The touch screens and diverse human-machine interaction methods of intelligent connected vehicles increase the possibility of visual distractions for drivers. Therefore, research on visual distraction in the context of intelligent connected vehicles (ICVs) holds substantial practical importance. Based on the human-machine interaction methods characteristics of intelligent connected vehicles, this paper designed and conducted a visual distraction driving simulation experiment, collecting data on driving performance, eye movement, and subjective workload evaluation data. Firstly, this paper designed a visual distraction driving sub-task and collected driving performance data, including vehicle speed, acceleration, and steering wheel angle speed, through a driving simulator. Simultaneously, the Tobii Glass 2 eye tracker was used to record pupil diameter data, and the NASA-TLX and SWAT subjective workload scales were employed to assess the driver's mental load. The driving performance, eye movement, and subjective workload characteristics of drivers with different driving experiences were analyzed when performing distraction tasks of varying difficulty. Finally, through one-way ANOVA and Pearson correlation coefficient analysis, five key parameters were identified as visual distraction indicators: longitudinal speed standard deviation, longitudinal acceleration standard deviation, lateral acceleration standard deviation, steering wheel angular velocity, and pupil diameter. The differences in control stability among drivers with different demographic characteristics under distracted conditions were also compared.