A consideration for real-time attention measurement for cooperative control between autonomous driving system and driver - Hiroe ABE; Luis DIAGO; Atsushi MINAMIHATA; Ichiro HAGIWARA (Nihon Kikai Gakkai ronbunshu)
We have repeatedly considered Holographic Neural Network (HNN) emerged by Sutherland ,J. G. as a causable machine learning. As a result, we developed FQHNN (Fuzzy Quantification Theory Embedded Holographic Neural Network) which has been already applied to various problems successfully because of its causality and versatility. It is important for the system itself to grasp the concentration of the driver especially in the case of auto-driving level 3 where the driver must appropriately respond to requests to intervene for driving from the system. We are progressing the research of the auto-driving car from the point of cooperation between the system and the driver with the thought that the system must continue the driving depending on the situation of the concentration of the driver. To carry out this research, it is realized that the causable machine learning holds the key of the success and here we develop FQHNN to the time series problem. It is confirmed the excellence of the FQHNN in the time series against LSTM (Long Short- Term Memory). We try to develop concentration confirmation system with facial expression analysis based on FQHNN in the time series. At last, we discuss whether the system can be applied to judge the concentration of the driver of auto-driving car in real time.