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Spatial Deep Learning Approach to Older Driver Classification.

Charles Boateng¹, Seyedeh Gol Ara Ghoreishi¹, Kwangsoo Yang¹

¹Florida Atlantic University, Boca Raton, USA.

IEEE Access : Practical Innovations, Open Solutions

|January 3, 2025

Summary

This summary is machine-generated.

This study introduces a new deep learning method using spatial data to accurately classify older drivers into normal and abnormal groups. This improves road safety and risk assessment for elderly drivers.

Keywords:

Older Driver Classification Spatial Deep Learning Trajectory Data Mining

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Area of Science:

Artificial Intelligence
Transportation Safety
Gerontology

Background:

Older driver classification is crucial for road safety, insurance, and interventions for cognitive decline.
Telematics data presents challenges due to volume and heterogeneity.
Existing methods struggle with complex, temporally-detailed vehicle datasets.

Purpose of the Study:

To develop a novel spatial deep-learning approach for accurate older driver classification.
To enhance the detection of abnormal driving behaviors using Grid-Index based data augmentation.
To address the challenges posed by large and heterogeneous telematics datasets.

Main Methods:

Proposed a novel spatial deep-learning model.
Implemented Grid-Index based data augmentation techniques.
Conducted extensive experiments and a real-world case study.

Main Results:

The proposed approach achieved high accuracy in identifying abnormal drivers.
Grid-based methods significantly improved telematics-based driving behavior analysis.
Demonstrated consistent performance across various tests.

Conclusions:

The spatial deep-learning approach effectively classifies older drivers.
Grid-index methods offer a promising way to enhance driving behavior analysis.
Findings support improved road safety, insurance assessment, and targeted interventions.