Detection of Driver Cognitive Distraction Using Machine Learning Methods

Abstract

Driver distraction is one of the primary causes of crashes. As a result, there is a great need to continuously observe driver state and provide appropriate interventions to distracted drivers. Cognitive distraction refers to the “look but not see” situations when the drivers’ eyes are focused on the forward roadway, but their mind is not. Typically, cognitive distractions can result from fatigue, conversation with a co-passenger, listening to the radio, or other similarly loading secondary tasks that do not necessarily take a driver’s eyes off the roadway. This makes it one of the hardest distractions to detect as there are no visible clues of driver distraction. In this study, we have identified features from different sources including eye-tracking, physiological, and vehicle kinematics data that are relevant towards the classification of distracted and non-distracted drivers via the analysis of data collected from a driving simulator study involving 40 drivers across multiple driving scenarios. The key classification algorithms implemented include Random Forest, Decision Trees and Support Vector Machines. A reduced feature set including pupil area, pupil vertical and horizontal motion was found to be predictive of driver distraction while maintaining an average accuracy of 90% across various road types. Additionally, the impact of road types on driver behaviour was also identified. The findings of the study has practical application towards the design of driver distraction monitoring systems.