Journal of the Korean Association of Geographic Information Studies (한국지리정보학회지)

The Korean Association of Geographic Information Studies (한국지리정보학회)
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A Study on Position Correction Sign for Autonomous Driving Vehicles

자율주행 자동차를 위한 측위 보정 표지 연구

  • Received : 2023.10.25
  • Accepted : 2023.12.13
  • Published : 2023.12.31
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Abstract

Autonomous driving vehicles recognize the surroundings through various sensors mounted on the vehicle and control the vehicle based on the collected information. The level of autonomous driving technology is improving due to the development of sensor technology and algorithms that process collected data, but the implementation of perfect autonomous driving technology has not been achieved. To overcome these limitations, through autonomous cooperative driving centered on infrastructure. In this study, developed a position correction sign that provides a reference for positioning of autonomous vehicles. First of all, an analysis was performed on the current status of positioning technology for autonomous driving. And measure the number of point clouds for the 1st sample consisting of two square reflective surfaces and 2nd sample that increased the vertical length of each reflective surface. Experimental results show that both primary and secondary products are installed at least 15 m apart It could be recognized as a sensor, and it was confirmed that the secondary production that increased the length of the top and bottom had a higher number of point clouds than the primary production and better expressed the shape of the facility.

자율주행차량은 차량에 장착된 다양한 센서를 통해 주행환경을 인지하고 수집된 정보를 기반으로 판단 및 제어한다. 센서 기술 및 수집된 데이터를 처리하는 알고리즘의 발달로 자율주행기술의 수준은 향상되고 있으나 완벽한 자율주행기술의 구현에는 이르지 못하고 있는 한계점을 인프라를 중심으로 하는 자율협력주행을 통해 극복하려는 움직임을 보인다. 본 연구에서는 자율주행차량의 측위를 보정할 수 있는 인프라로서 기준을 제공하는 측위 보정 표지를 개발하였다. 우선 기존의 자율주행을 위한 측위 기술 현황에 대한 분석을 수행하였다. 다음으로 정사각형의 반사면 두 개로 구성된 1차 제작물과 각 반사면의 상하 길이를 늘인 2차 제작물에 대해 포인트 클라우드 개수를 측정하는 실험을 수행하였다. 실험 결과 1차 및 2차 제작물 모두 최소 15m 거리에서 시설물을 라이다 센서로 인식할 수 있었고, 상하 길이를 확장한 2차 제작물이 1차 제작물보다 포인트 클라우드 개수도 더 많으며 시설물의 형상을 구체적으로 표현하는 것을 확인할 수 있었다.

Keywords

Acknowledgement

본 연구는 국토교통부/국토교통과학기술진흥원의 지원으로 수행되었음(과제번호 22AMDP-C161924-02).

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