  This paper focuses on the estimation of a driver's psychological
characteristics using driving data for driving assistance systems. Driving
assistance systems that support drivers by adapting individual psychological
characteristics can provide appropriate feedback and prevent traffic accidents.
As a first step toward implementing such adaptive assistance systems, this
research aims to develop a model to estimate drivers' psychological
characteristics, such as cognitive function, psychological driving style, and
workload sensitivity, from on-road driving behavioral data using machine
learning and deep learning techniques. We also investigated the relationship
between driving behavior and various cognitive functions, including the Trail
Making Test (TMT) and Useful Field of View (UFOV) test, through regression
modeling. The proposed method focuses on road type information and captures
various durations of time-series data observed from driving behaviors. First,
we segment the driving time-series data into two road types, namely, arterial
roads and intersections, to consider driving situations. Second, we further
segment data into many sequences of various durations. Third, statistics are
calculated from each sequence. Finally, these statistics are used as input
features of machine learning models to estimate psychological characteristics.
The experimental results show that our model can estimate a driver's cognitive
function, namely, the TMT~(B) and UFOV test scores, with Pearson correlation
coefficients $r$ of 0.579 and 0.708, respectively. Some characteristics, such
as psychological driving style and workload sensitivity, are estimated with
high accuracy, but whether various duration segmentation improves accuracy
depends on the characteristics, and it is not effective for all
characteristics.
