자동차안전학회지 (Journal of Auto-vehicle Safety Association)

한국자동차안전학회 (Korean Auto-vehicle Safety Association)
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자율주행 인지 모듈의 실시간 성능을 위한 적응형 관심 영역 판단

An Adaptive ROI Decision for Real-time Performance in an Autonomous Driving Perception Module

  • 이아영 (서울대학교 기계공학부) ;
  • 이호준 (서울대학교 기계공학부) ;
  • 이경수 (서울대학교 기계공학부)
  • 투고 : 2020.11.10
  • 심사 : 2022.02.24
  • 발행 : 2022.06.30
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초록

This paper represents an adaptive Region of Interest (ROI) decision for real-time performance in an autonomous driving perception module. Since the whole automated driving system consists of numerous modules and subdivisions of module occur, it is necessary to consider the characteristics, complexity, and limitations of each module. Furthermore, Light Detection And Ranging (Lidar) sensors require a considerable amount of time. In view of these limitations, division of submodule is inevitable to represent high real-time performance for stable system. This paper proposes ROI to reduce the number of data respect to computation time. ROI is set by a road's design speed and the corresponding ROI is applied differently to each vehicle considering its speed. The simulation model is constructed by ROS, and overall data analysis is conducted by Matlab. The algorithm is validated using real-time driving data in urban environment, and the result shows that ROI provides low computational costs.

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과제정보

본 연구는 국토교통부 도심도로 자율협력주행 안전·인프라 연구 사업의 연구비지원(과제번호 19PQOW-B152473-01)에 의해 수행되었습니다.

참고문헌

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