[3D1-GS-9] Human interface:

[3D1-GS-9-02] Estimation of the Degree of Self-Awareness of Changes in Cognitive Function Based on In-Vehicle Sensor Data

〇Yusuke Shimoi¹, Yuki Fujita¹, Ryusei Kimura¹, Hiroto Horimoto¹, Takahiro Tanaka², Shogo Okada¹ (1. Japan Advanced Institute of Science and Technology, 2. Nagoya University)

Keywords:Self-Awareness of Changes in Cognitive Function, In-Vehicle Sensor, Machine Learning

It is known that there is a discrepancy between cognitive functions measured using objective indicators and those assessed through self-evaluation and that individuals with a larger discrepancy are more likely to be involved in traffic accidents. In this study, we estimated the degree of self-awareness change in cognitive function from in-vehicle sensor data obtained via the Controller Area Network (CAN) using three machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). As a result, for the "Change in Ability to Assess Traffic Conditions" item from the Self-Awareness Questionnaire on Elderly Driving Characteristics, the values on major roads were 0.507 for LR, 0.646 for SVM, and 0.726 for RF, while at intersections they were 0.767 for LR, 0.808 for SVM, and 0.258 for RF, demonstrating that this characteristic can be estimated with relatively high accuracy.