Detection of driver distraction based on temporal relationship between eye-gaze and peripheral vehicle behavior

One's state of mind is subconsciously exposed as a reaction reflecting it by external stimuli. In this work, we focus on a car driver's cognitive distraction, specifically by analyzing a driver's internal state induced during a music-retrieval task using an automatic speech-recognition system. 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. The analysis result showed that the temporal relationship between the driver's eye gaze and the peripheral vehicle behavior depends on the driver's state. Specifically, we confirmed that the timing of the gaze toward the stimulus under the distracted state is later than under the neutral state without the secondary cognitive task. This temporal feature can contribute to the detection of 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.