The ability of eye-tracking metrics to classify and predict the perceived driving workload

Abstract

Traffic safety is directly related to the mental and physical condition of the driver. Performing regular secondary tasks while driving is an additional activity that dissipates attention and adds to the drivers' workload. Identifying driver fatigue and workload based on gaze behavior is one way to ensure a safe driving experience. The purpose of this paper is to classify and predict driving perceived workload using a set of eye-tracking metrics (gaze fixation, duration, pointing, and pupil diameter). The ability of eye-tracking metrics to predict driving workload has been investigated. As a result, frustration, performance, and temporal load showed a correlation with gaze metrics. Gaze point, duration, fixation, and pupil diameter significantly influence driving workload.Relevance to industry: Results will supply the specialists in eye-tracking/sensor technologies and traffic safety with new knowledge to improve the design of the driving performance and safety monitoring systems and efficiency of the driving process.