Estimation of Cognitive Function from Driving Data

Abstract

Traffic accidents by older drivers due to cognitive decline have become a serious problem. Driving assistance systems that support the driver by adapting individual cognitive functions can provide appropriate feedback and prevent traffic accidents. To realize such systems, we developed a regression model to estimate a driver's cognitive function from on-road driving data. First, we segment driving time-series data into two road types, namely, arterial road and intersections, to consider driving situations. Second, we segment data further into many sequences with various duration. Finally, statistics are calculated from each sequence and they are used as input features of machine learning models. Our method can capture various duration of important driving behaviors. The experimental results show that our model can predict scores of Trail Making Test B and Useful Field of View test with $r$ of 0.747 and 0.634, respectively. Additionally, we reveal important sensor and road types for estimation.