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This study proposes an environment-adaptive sensor weight fusion model for autonomous driving systems that automatically optimizes the weights between LiDAR and camera sensors based on environmental conditions. Using an F1TENTH autonomous driving platform, we implemented a dual-branch neural network that processes data from 2D LiDAR and monocular camera sensors and combines their outputs with learnable weights. Through extensive experiments in various indoor environments, we found that the optimal sensor weight ratio in general conditions was approximately 64% for LiDAR and 36% for camera sensors. However, these weights dynamically adjusted based on environmental changes, with LiDAR reliance increasing in low-light conditions and camera influence growing in well-lit environments. Our results highlight the importance of dynamic sensor fusion in autonomous driving systems and provide insights into optimal weight distributions across different driving scenarios. Future work will extend this approach to larger-scale platforms with additional sensors such as GPS.

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Abstract
1. 서론
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저자

• 박세훈 [ Se-hoon Park | Associate Professor, Dept. of AI Software, Inje University ] Corresponding Author
• 김경이 [ Kyoung-yee Kim | M.S., Dept. of AI Convergence Robotics, Inje University ]
• 김성우 [ Seong-woo Kim | Same affiliation as above ]
• 최재형 [ Jae-hyung Choi | Same affiliation as above ]

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• 간행물명

  한국기계항공기술학회 학술대회논문집(구 한국기계기술학회 학술대회논문집) [Proceedings of KSMT Annual Meeting]

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• 수록기간

  2000~2026

• 십진분류

  KDC 550 DDC 620