主催: 横断型基幹科学技術研究団体連合
会議名: 第11回横幹連合コンファレンス
回次: 11
開催地: 統計数理研究所（東京都立川市）
開催日: 2020/10/08 - 2020/10/09
One of the expectations towards the automated driving technology is dramatic reduction of road crashes. The background hypothesis is that replacing the human driver by a computer will eliminate crashes that are currently caused by human errors. However, complication of the systems and new tasks imposed on the driver may generate new risks, system-induced problems. The main focus of human factors research in automated driving is to reduce the system-induced problems. The research project was conducted from FY2016 to FY2018 with the funding awarded by Cabinet Office and SIP-adus Phase 1. One of the three tasks of the project aimed at understanding effects of cognitive states of the driver on his/her takeover performance and extracting metrics of the influential driver states for driver monitoring systems. It was found that different driver states influenced takeover performance in different ways. Some metrics for the influencing states were identified. These findings are being discussed and applied to International Standards.
