Readiness Estimation for a Take-Over Request in Automated Driving on an Expressway

Automated driving is attracting attention as a solution to road traffic problems. At Level 3, a take-over request (TOR) is issued to transfer driving operations from the system to a driver when it is unable to continue. In such cases, the driver must be monitored to ensure a proper takeover of the driving operations. This study aims to measure drivers' brain activity before and after the TOR by analyzing time-series signals of brain activity with machine learning algorithms. We developed driving scenarios with a TOR trigger on a rainy expressway at night. We used a portable functional near-infrared spectroscopy (fNIRS) device to measure cerebral blood oxygenation changes (Delta HbO) at the frontal pole. We used a long short-term memory (LSTM) network on this data for time-series learning and prediction after multivariate and multilayering modifications to improve accuracy. We conducted driving questionnaires beforehand and used two classification methods to categorize subjects into several groups with similar driving characteristics. Experimental results of a Delta HbO drop revealed that brain activity tended to decrease during automated driving. Moreover, success in obstacle avoidance and mean squared error (MSE) for each driver group demonstrated that the behavior toward an obstacle after the TOR trigger influenced changes in brain activity.