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    정교한 환경 및 센서 모델링을 통한 VIL 시뮬레이션 충실도 향상 = VIL Simulation Fidelity Improvement through Realistic Environment and Sensor Modeling

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    https://www.riss.kr/link?id=T17557250

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    This paper proposes a framework to enhance the fidelity of Vehicle-in-the-Loop (VIL) simulation by introducing realistic sensor modeling and environmental reconstruction. Despite reflecting real vehicle dynamics, conventional VIL has limitations in that simplified sensor emulation and generalized virtual environments create a gap between simulation and reality (Sim-to-Real Gap).
    To overcome this, this study reproduces LiDAR beam patterns and packet structures, and constructs a realistic environment based on point cloud maps using the Poisson Surface Reconstruction technique. Furthermore, to analyze the individual contributions of sensor and environmental realism, five configurations were evaluated in Euro NCAP Car-to-Car scenarios, and the simulation fidelity was quantified using the normalized RMSE and Pearson correlation coefficient of six key signals.
    Experimental results demonstrated that environmental realism yielded the greatest improvement in fidelity by stabilizing positioning through accurate geometric reconstruction. Realistic LiDAR modeling played a complementary, fine-tuning role in improving perception accuracy, and the final integrated configuration achieved a fidelity close to the ground truth. Consequently, the findings suggest that maximizing the realism of both sensor and environmental domains is essential for the reliable and efficient validation of autonomous driving systems.
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    This paper proposes a framework to enhance the fidelity of Vehicle-in-the-Loop (VIL) simulation by introducing realistic sensor modeling and environmental reconstruction. Despite reflecting real vehicle dynamics, conventional VIL has limitations in th...

    This paper proposes a framework to enhance the fidelity of Vehicle-in-the-Loop (VIL) simulation by introducing realistic sensor modeling and environmental reconstruction. Despite reflecting real vehicle dynamics, conventional VIL has limitations in that simplified sensor emulation and generalized virtual environments create a gap between simulation and reality (Sim-to-Real Gap).
    To overcome this, this study reproduces LiDAR beam patterns and packet structures, and constructs a realistic environment based on point cloud maps using the Poisson Surface Reconstruction technique. Furthermore, to analyze the individual contributions of sensor and environmental realism, five configurations were evaluated in Euro NCAP Car-to-Car scenarios, and the simulation fidelity was quantified using the normalized RMSE and Pearson correlation coefficient of six key signals.
    Experimental results demonstrated that environmental realism yielded the greatest improvement in fidelity by stabilizing positioning through accurate geometric reconstruction. Realistic LiDAR modeling played a complementary, fine-tuning role in improving perception accuracy, and the final integrated configuration achieved a fidelity close to the ground truth. Consequently, the findings suggest that maximizing the realism of both sensor and environmental domains is essential for the reliable and efficient validation of autonomous driving systems.

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    목차 (Table of Contents)

    • 제1장 서론 1
    • 제2장 환경 및 센서 모델링 파이프라인 8
    • 제1절 센서 모델링 8
    • 1. 중요성 8
    • 2. 라이다 파라미터 9
    • 제1장 서론 1
    • 제2장 환경 및 센서 모델링 파이프라인 8
    • 제1절 센서 모델링 8
    • 1. 중요성 8
    • 2. 라이다 파라미터 9
    • 3. 빔 패턴 10
    • 제2절 환경 모델링 11
    • 1. 중요성 11
    • 2. SLAM 및 GNSS 측량 12
    • 3. 도로 중심선 생성 12
    • 4. 지도-중심선 정렬 12
    • 5. 법선 벡터 계산 13
    • 6. 결과 및 한계점 14
    • 7. 표면 재구성 15
    • 제3장 실험 구성 및 검증 시나리오 16
    • 제1절 실험 구성 16
    • 1. VIL 시스템 개요 16
    • 2. 시스템 구성 요소 및 데이터 흐름 18
    • 3. 테스트 케이스 구성 19
    • 제2절 검증 시나리오 20
    • 1. 테스트 프로토콜 20
    • 2. 시나리오 구현 22
    • 3. 평가 지표 23
    • 제4장 실험 결과 및 분석 24
    • 제1절 동기화 방식의 검증 24
    • 제2절 케이스별 분석 25
    • 제3절 시나리오별 분석 32
    • 제4절 토의 및 한계점 33
    • 제5절 요약 33
    • 제5장 결론 34
    • 참고문헌 35
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