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Multimodal Transformer Model Using Time-Series Data to Classify Winter Road Surface Conditions.

Yuya Moroto1, Keisuke Maeda2, Ren Togo3

  • 1Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Japan.

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Summary

This study introduces a multimodal Transformer model for detecting and predicting winter road conditions using time-series data. The model effectively integrates various data sources, improving accuracy for road surface condition monitoring.

Keywords:
deep learningmultimodal analysistime-series processingtransformerwinter road surface condition

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Transportation Engineering

Background:

  • Previous methods for road surface condition analysis often use simple integration of multiple data types like images and auxiliary data.
  • While multimodal approaches improve performance, effective integration of heterogeneous data remains a challenge.

Purpose of the Study:

  • To propose a novel multimodal Transformer model for enhanced detection and prediction of winter road surface conditions.
  • To improve the integration of heterogeneous data modalities for more accurate road condition analysis.

Main Methods:

  • Utilized a multimodal Transformer architecture incorporating time-series data.
  • Implemented a cross-attention mechanism for effective feature integration and compensation between modalities.
  • Incorporated time-series processing to capture temporal changes in road surface conditions.

Main Results:

  • The proposed method demonstrated effectiveness in both detecting and predicting winter road surface conditions.
  • Experimental results confirmed the superiority of the enhanced modality integration technique.
  • The model achieved improved representational ability of integrated features.

Conclusions:

  • The multimodal Transformer model offers a significant advancement in analyzing and predicting winter road surface conditions.
  • Effective integration of heterogeneous data through cross-attention and time-series analysis is crucial for improving performance.
  • The approach holds promise for enhancing road safety and traffic management during winter conditions.