2 Papers Published

“Quad-tree Based Driver Classification using Deep Learning for Mild Cognitive Impairment Detection”

Given GPS points on a transportation network, the goal of the Quad-tree Based Driver Classification (QBDC) problem is to identify whether drivers have Mild Cognitive Impairment (MCI). The QBDC problem is challenging due to the large volume and complexity of the data. This paper proposes a quad-tree based approach to the QBDC problem by analyzing driving patterns using a real-world dataset. We propose a geo-regional quad-tree structure to capture the spatial hierarchy of driving trajectories and introduce new driving features representation for input into a convolutional neural network (CNN) for driver classification. The experimental results demonstrate the effectiveness of the proposed algorithm, achieving an F1 score of 95% that significantly outperforms the baseline models. These results highlight the potential of geo-regional quad-tree structures to extract interpretable features and describe complex …

“In-Vehicle Sensing Platform for the Inference of Older Drivers’ Mild Cognitive Condition”

Changes in the driving behavior of older drivers can be indicative of conditions of mild cognitive impairment (MCI), which affect their memory and recognition skills on the road. Traditional clinical evaluations cover only a limited subset of cognitively impaired drivers, prompting the need for innovative technologies to monitor the cognitive status of older drivers routinely. In this study, we developed in-vehicle sensing devices capable of capturing vehicular data streams that reveal older drivers’ driving patterns. Using K-means clustering on preprocessed and scaled data, we identified four distinct driver profiles characterized by trip frequency, driving style, and demographic factors. These profiles ranged from active, frequent travelers to sedentary, cautious drivers, with significant differences in trip duration, distance, and vehicle operation metrics such as speed and engine load. A developed random forest model further identified peak hour trips, age, gender, and ambient temperature as significant predictors of MCI, highlighting the complex interplay between lifestyle, driving behaviors, and demographics.