Driver Vigilance State Estimation Based on Multisource Data

Abstract

Accurate estimation of driver vigilance state is important for improving driving safety and preventing accidents. Previous studies have extensively investigated the potential of driving performance measures (e.g., lane position, steering wheel angle, and lateral acceleration) and eye movement measures (e.g., glance frequency and duration) on the driver’s vigilance state estimation. However, the change in these measures may be caused by various factors. Therefore, comprehensively involving more direct measures (e.g., drivers’ electroencephalogram (EEG) response) is expected to increase the vigilance estimation performance. This study comprehensively uses measures across different functional domains (i.e., driving performance, eye movement, and EEG) to establish an estimation model of the driver’s vigilance state. Twenty participants were assigned a 2-h sustained-attention driving task in a simulator, in which they followed a lead car closely. According to their brake reaction times (RTs) to the brake signal of the lead vehicle, their vigilance states were divided into three levels (i.e., low, medium, and high). A logistic regression model that estimates drivers’ vigilance state level based on multisource data was established. This model is a promising approach to driver states detection and reveals the factors that should be considered in subsequent model development processes. It can be combined with driver vigilance arousal methods to improve driving safety.