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IEEE/ICACT20250226 Slide.17        [Big Slide]	Oral Presentation
	To conclude, we introduced the Adaptive Cross-Attention Gated Network (ACAGN) for enhanced radar-camera fusion method in BEV space. Our proposed method integrates deformable cross-attention and a gated network mechanism to dynamically align and prioritize multimodal features. Our proposed method integrates deformable cross-attention and adaptive gated network. This addressed the challenges of spatial misalignment and feature integration. We also demonstrated robustness in complex and adverse weather driving scernarios such as day, night, and rain conditions in weather analysis. We achieved improvement over the baseline, with +0.2 increase in NDS (NuScenes Detection Score) and 0.3 increase in mAP (mean Average Precision) on the nuScenes dataset. For future work, fusion based occupancy mapping or trajectory planning using BEV will be researched.

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