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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.

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Summary
This summary is machine-generated.

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.

Keywords:
GPS dataSpatiotemporal dataattention mechanismdeep learningdriving behaviorolder driver classificationtrajectory analysisvision transformer

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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.