To prevent accidents associated with a driver’s sleepiness, it is imperative to estimate the driver’s arousal level and manage the arousal level appropriately. Recently, methods to maintain arousal levels have been diversified; particularly, a method through voice interaction with an artificial intelligence agent has been proposed. In such a method, it is thought that it is effective to set the frequency and content of interactions according to arousal level, but the multistage arousal estimation using face images, which are relatively easy to measure, remains challenging. In this study, aiming at application to real-time feedback, using face images, we consider a multistage arousal level estimation that is independent of the individual. Through experiments, we obtained data on the subjective assessment of sleepiness and face images and constructed models via deep learning. From the model evaluation, learning considering timeseries characteristics is effective in multistage arousal level estimation. With 67.1% and 68.6% data, respectively, for long short-term memory and gated recurrent unit methods, the deviation between the actual and estimated levels can be suppressed to ≤1, suggesting that a rough multistage estimation model can be realized with these methods.
抄録全体を表示
