Analysis of peripheral vehicular behavior in driver's gaze transition: Differences between driver's neutral and cognitive distraction states

Abstract

To support safe driving, numerous methods have been proposed for detecting distractions based on the measure- ments of a driver's gaze. These methods empirically focused on certain driving contexts, and analyzed gaze behavior under particular peripheral vehicular conditions; therefore, numerous driving situations were not considered. To address this problem, we propose a data-driven approach that analyzes peripheral vehicular behaviors during gaze transitions of drivers, to compare their neutral driving state with a cognitive distraction state. The analysis results show that drivers, under the neutral conditions, turned their gaze to peripheral vehicles to be focused on; however, they did not do this consistently under the distracted conditions. In addition, we propose a simple classifier to discriminate between the distracted and the neutral states by analyzing peripheral vehicular behavior. The proposed classifier can manage various situations, and provide high discrimination accuracy, by focusing on gaze transitions from the front view toward other directions. I. INTRODUCTION Advanced Driver Assistance Systems (ADAS) and autopi- lot vehicles have attracted significant attention. However, researchers are concerned that drivers may develop an over- reliance on incomplete systems, which may lead to accidents. To prevent overreliance, ADAS must effectively analyze the driver's state as well as traffic situations. In this study, we focus on driver's cognitive distraction, which is an observable state resulting from the overreliance, and its relationships with driver's eye-gaze and peripheral vehicular behaviors. Driver distraction is a diversion of attention away from activities critical for safe driving toward a competing activity(1), and is a significant risk factor that can cause accidents(2). Note that distraction differs from fatigue(3), which is defined as a state of exhaustion that disables a person from continuing an activity(4). Numerous researchers have developed driver distraction monitoring systems that aim to promote driving safety by considering different types and levels of distraction(3). The National Highway Traffic Safety Administration (NHTSA) classifies distractions into the following categories: (1) visual distraction; (2) auditory distraction; (3) biomechanical distraction; and (4) cognitive distraction from the viewpoint of the driver's functionality(2). Visual distraction, auditory distraction, and biomechanical distraction are caused by external factors that disturb the