A car driver’s cognitive distraction is a main factor behind car accidents. One’s state of mind is subconsciously exposed as a reaction reflecting it by external stimuli. A visual event that occurs in front of the driver when a peripheral vehicle overtakes the driver’s vehicle is regarded as the external stimulus. We focus on temporal relationships between the driver’s eye gaze and the peripheral vehicle behavior. The analysis result showed that the temporal relationships depend on the driver’s state. In particular we confirmed that the timing of the gaze toward the stimulus under the distracted state induced by a music retrieval task using an automatic speech recognition system is later than that under a neutral state while only driving without the secondary cognitive task. This temporal feature can contribute to detecting the cognitive distraction automatically. A detector based on a Bayesian framework using this feature achieves better accuracy than one based on the percentage road center method. 1. Introduction Driver distraction is a diversion of attention away from activities critical for safe driving toward a competing activity [1] and is a large risk factor that causes accidents [2]. Note that distraction differs from fatigue [3] which is defined as a state that disables one from continuing the activity [4]. Many researchers have developed driver distraction monitoring systems to maintain safety while driving by considering different types and levels of distraction [3]. The National Highway Traffic Safety Administration (NHTSA) classifies distractions into (1) cognitive distraction, (2) visual distraction, (3) auditory distraction, and (4) biomechanical distraction from the viewpoint of the driver’s functionality [2]. Cognitive distraction can be considered as an internal state of the driver. It is difficult to sense this from outside. The other distractions are external factors that disturb the activity and can be observed more easily. We focus on cognitive distraction and seek novel findings to automatically detect it. In the past few decades, a number of methods for detecting distraction have been proposed [3]. The methods fall into the following five categories based on the types of measures: (1) subjective report measures, (2) driver biological measures, (3) driving performance measures, (4) driver physical measures, and (5) hybrid measures. Among these measures, subjective report measures and driver biological measures are not suitable under real driving conditions. Driving performance measures as indicated by steering, braking behavior,
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