기존의 인프라 데이터셋은 주로 고가의 라이다 센서를 활용하거나 완벽하게 통제된 환경에서만
제한적으로 구축되어, 막대한 시간과 비용이 소모된다는 한계가 있다. 이에 본 논문은 도심에 이미
설치된 일반적인 단안 CCTV 영상만을 3D 조감도로 변환하여, 저비용·고효율로 궤적 예측 모델 학
습용 데이터셋을 구축할 수 있는 자동화 파이프라인을 제안한다. 제안하는 방법은 YOLOv8x-
visdrone 과 ByteTrack 을 활용한 객체 탐지 및 추적, 학습 기반 RANSAC 을 통한 3D 월드 좌표 변환,
정지 차량 방향각 보정 및 벡터 맵 통합 과정을 거쳐 정밀한 데이터를 추출한다. 최종적으로 구축
된 데이터셋을 최신 벤치마크 모델인 HiVT, V2X-Graph 및 본 연구에서 제안하는 모델에 적용해 실
효성을 평가하였으며, 특히 벡터 맵을 결합한 제안 모델이 비교 모델 중 가장 우수한 예측 성능을
달성함을 확인하였다.
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