Quad-Tree-Based Driver Classification Using Deep Learning for Mild Cognitive Impairment Detection

doi:10.1109/ACCESS.2025.3558706

Quad-Tree-Based Driver Classification Using Deep Learning for Mild Cognitive Impairment Detection

2025-01-01

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Seyedeh Gol Ara Ghoreishi *

DOI：10.1109/ACCESS.2025.3558706

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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 driving patterns. This approach offers significant implications for driver classification, with the potential to improve road safety and cognitive health monitoring.

Keywords:

Trajectory

Vehicles

Global Positioning System

Driver behavior

Convolutional neural networks

Deep learning

Shape

Road safety

Feature extraction

Telematics

Spatiotemporal data

GPS data

trajectory analysis

driving behavior

older driver classification

quad-tree decomposition

convolutional neural networks

Quad-Tree-Based Driver Classification Using Deep Learning for Mild Cognitive Impairment Detection

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