인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Robust and efficient 3D perception is critical for autonomous vehicles operating in complex environments. Multi-sensor fusion, such as Camera+LiDAR, Camera+Radar, or all three modalities, significantly enhances scene understanding, However, most existing frameworks fuse data at a fixed stage, categorized as early fusion (raw data level), mid fusion (intermediate feature level), or late fusion (detection output level), neglecting semantic consistency across modalities. This static strategy may result in performance degradation or unnecessary computation under sensor misalignment or noise. In this work, we propose FDSNet (Feature Disagreement Score Network), a dynamic fusion framework that adaptively selects the fusion stage based on measured semantic consistency across sensor modalities. Each sensor stream (Camera, LiDAR, and Radar) independently extracts mid-level features, which are then transformed into a common Bird’s Eye View (BEV) representation, ensuring spatial alignment across modalities. To assess agreement, a Feature Disagreement Score (FDS) is computed at each BEV location by measuring statistical deviation across modality features. These local scores are aggregated into a global FDS value, which is compared against threshold to determine the fusion strategy. A low FDS, indicating strong semantic consistency across modalities, triggers mid-level fusion for computational efficiency, whereas a high FDS value activates late fusion to preserve detection robustness under cross-modal disagreement. We evaluate FDSNet on the nuScenes dataset across multiple configurations: Camera+Radar, Camera+LiDAR, and Camera+Radar+LiDAR. Experimental results demonstrate that FDSNet achieves consistent improvements over recent multimodal baselines, with gains of up to +3.0% in NDS and +2.6% in mAP on the validation set, and +2.1% in NDS and +1.6% in mAP on the test set, highlighting that dynamic stage selection provides both robustness and quantifiable advantages over static fusion strategies.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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