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Abnormal Driving Pattern Detection from GPS Trajectories Using Vision Transformer
Seyedeh Gol Ara Ghoreishi1, Kwangsoo Yang1
1Computer Science, Florida Atlantic University, Boca Raton, FL, USA.
Research Square
|February 12, 2026
Summary
This study introduces a new method using binary grid images to analyze driving patterns for driver classification. The approach effectively identifies normal versus abnormal driving behavior, enhancing road safety.
Area of Science:
- Computer Science
- Artificial Intelligence
- Transportation Engineering
Background:
- The Driving Pattern Detection (DPD) problem faces challenges in standardizing diverse driving data (trip length, routes, spatial patterns) for deep learning.
- Variability in driving behavior complicates accurate classification of drivers as normal or abnormal.
Purpose of the Study:
- To develop a novel spatial representation learning framework for the Driving Pattern Detection problem.
- To improve the accuracy of classifying drivers based on their driving behavior using deep learning models.
Main Methods:
- Proposed a novel framework using binary grid images to represent the spatial structure of driving trajectories.
- Utilized a Vision Transformer (ViT) model for driver classification based on the new spatial representation.
- Analyzed driving patterns using a real-world dataset.
Main Results:
- Achieved a high F1 score of 94% in driver classification, significantly outperforming baseline models.
- Demonstrated that binary grid representations effectively encode interpretable spatial patterns of driving behavior.
- The proposed method shows significant improvements in driver classification accuracy.
Conclusions:
- Binary grid representations offer an effective method for capturing spatial driving patterns crucial for driver classification.
- The developed framework has direct relevance for enhancing road safety and potentially assessing cognitive health.
- This approach provides a robust solution to the challenges posed by data variability in DPD.
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