運転者の生体・行動・主観的指標を用いた運転負担推定に関する研究

～半教師あり学習による識別モデルの高精度化～

抄録

It has been reported that the driver workload is a cause of drowsiness, fatigue, and accidents of the driver, and a system for estimating the driver workload is required. In the conventional study, we have developed a system that estimates the current and future driver workload by using LSTM (Long Short-Term Memory). However, this system used an ambiguous subjective evaluation index for the teacher label, which had the problem of adversely affecting the discrimination accuracy. Therefore, in this study, we propose a system that can estimate the current and future driver workload with high accuracy by removing the ambiguity of subjective evaluation using semi-supervised learning and logistic regression. The experimental results show that the proposed system is superior in discrimination accuracy to the conventional study.