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

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...

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Bibliographic Details
Main Authors: Seyedeh Gol Ara Ghoreishi, Charles Boateng, Sonia Moshfeghi, Muhammad Tanveer Jan, Joshua Conniff, Kwangsoo Yang, Jinwoo Jang, Borko Furht, David Newman, Ruth Tappen, Monica Rosselli, Kelley L. Jackson
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Spatiotemporal data
GPS data
trajectory analysis
driving behavior
older driver classification
quad-tree decomposition
Online Access:https://ieeexplore.ieee.org/document/10955159/
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https://ieeexplore.ieee.org/document/10955159/

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