DOI QR코드

DOI QR Code

도로교통 이머징 리스크 탐지를 위한 AutoML과 CNN 기반 소프트 보팅 앙상블 분류 모델

AutoML and CNN-based Soft-voting Ensemble Classification Model For Road Traffic Emerging Risk Detection

  • 전병욱 (경기대학교 AI컴퓨터공학부) ;
  • 강지수 (경기대학교 컴퓨터과학과) ;
  • 정경용 (경기대학교 AI컴퓨터공학부)
  • Jeon, Byeong-Uk (Division of AI Computer Science and Engineering, Kyonggi University) ;
  • Kang, Ji-Soo (Department of Computer Science, Kyonggi University) ;
  • Chung, Kyungyong (Division of AI Computer Science and Engineering, Kyonggi University)
  • 투고 : 2021.05.15
  • 심사 : 2021.07.20
  • 발행 : 2021.07.28

초록

겨울철 도로 결빙으로 인한 사고는 대부분 큰 사고로 이어진다. 이는 운전자가 도로의 결빙을 사전에 자각하기 어렵기 때문이다. 본 연구에서는 AutoML과 CNN의 앙상블 모델을 이용하여 도로교통 이머징 리스크를 정확하게 탐지하는 방법을 연구한다. 비정형 데이터인 이미지를 이용한 CNN 이미지 특징 추출 기반 도로교통 이머징 리스크 분류 모델과 정형 데이터인 기상 데이터를 이용한 AutoML 기반 도로교통 이머징 리스크 분류 모델을 각각 학습시킨다. 그 후 모델들에서 도출된 확률값을 입력하여 CNN 기반 분류 모델을 보완하도록 앙상블 모델을 설계한다. 이를 통해 도로교통 이머징 리스크 분류 성능을 향상하고 더 정확하고 빠르게 운전자에게 경고하여 안전한 주행이 가능하도록 한다.

Most accidents caused by road icing in winter lead to major accidents. Because it is difficult for the driver to detect the road icing in advance. In this work, we study how to accurately detect road traffic emerging risk using AutoML and CNN's ensemble model that use both structured and unstructured data. We train CNN-based road traffic emerging risk classification model using images that are unstructured data and AutoML-based road traffic emerging risk classification model using weather data that is structured data, respectively. After that the ensemble model is designed to complement the CNN-based classification model by inputting probability values derived from of each models. Through this, improves road traffic emerging risk classification performance and alerts drivers more accurately and quickly to enable safe driving.

키워드

과제정보

This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 21CTAP-C157011-02).

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