Active Vision-Based Attention Monitoring System for Non-Distracted Driving

Abstract

Inattentive driving is a key reason of road mishaps causing more deaths than speeding or drunk driving. Research efforts have been made to monitor drivers' attentional states and provide support to drivers. Both invasive and non-invasive methods have been applied to track driver's attentional states, but most of these methods either use exclusive equipment which are costly or use sensors that cause discomfort. In this paper, a vision-based scheme is proposed for monitoring the attentional states of the drivers. The system comprises four major modules-cue extraction and parameter estimation, state of attention estimation, monitoring and decision making, and level of attention estimation. The system estimates the attentional level and classifies the attentional states based on the percentage of eyelid closure over time (PERCLOS), the frequency of yawning and gaze direction. Various experiments were conducted with human participants to assess the performance of the suggested scheme, which demonstrates the system's effectiveness with 92% accuracy.