Journal of Auto-vehicle Safety Association (자동차안전학회지)

Korean Auto-vehicle Safety Association (한국자동차안전학회)
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Visualization of Vehicle Driving Patterns Using the LSTM-Autoencoder Algorithm with Attention Applied

Attention이 적용된 LSTM-Autoencoder 알고리즘을 사용한 차량 주행 패턴 시각화

  • Su-cheon Lee ;
  • Tae-geol Woo ;
  • Dong-hoon Shin ;
  • Kang-moon Park
  • 이수천 (한국교통대학교) ;
  • 우태걸 (한국교통대학교) ;
  • 신동훈 (한국해양대학교) ;
  • 박강문 (한국교통대학교)
  • Received : 2024.01.11
  • Accepted : 2024.08.31
  • Published : 2024.09.30
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Abstract

Recent advancements in vehicle driving data analysis have attracted significant attention as a key research area aimed at optimizing driving behavior and enhancing vehicle performance. This study aims to collect operational data from drivers, including steering angle, longitudinal acceleration, brake usage, and wheel speed, and to cluster driving segments based on their characteristics using deep learning techniques. To achieve this, we employed a methodology that integrates the LSTM-Autoencoder model, which captures essential features of time-series data through compression and reconstruction, with an Attention mechanism. The driving segments were clustered and visually represented with distinct colors for clarity. Experimental results from the proposed approach demonstrated an over 96% accuracy in aligning input and output values, facilitating the clustering of driving segments based on their distinctive features. These findings provide valuable insights for improving driver behavior and optimizing vehicle assistance systems, potentially contributing to significant advancements in this field of research.

Keywords

Acknowledgement

이 논문은 정부(과학기술정보통신부)의 제원으로 한국연구재단의 지원을 받아 수행된 연구임(No.2022R1G1A10070561230382068210102).

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