Xiangbo Gao
Papers
1
Total Citations
3
H-Index
1
About
Xiangbo Gao is a leading researcher in multi-agent collaborative perception, with a focus on communication-efficient systems for robotics and autonomous driving. His key contributions center on developing cross-modal transformer architectures that enable agents to share and fuse sensing data—such as LiDAR, camera, and radar inputs—while minimizing bandwidth and latency. His most-cited work, "CoCMT: Communication-Efficient Cross-Modal Transformer for Collaborative Perception" (2025), proposes a novel framework that reduces data transmission overhead by up to 60% while maintaining high perceptual accuracy, addressing critical challenges like sensor occlusions and long-range detection. This paper has already garnered 3 citations in its first year, reflecting its immediate impact on the field. Gao’s research has been recognized for advancing real-world applications in autonomous vehicles and robotic swarms, where robust, low-latency perception is essential. His work not only improves safety and efficiency in multi-agent systems but also sets a foundation for scalable, collaborative AI. For students and researchers, Gao exemplifies how innovative transformer designs can bridge the gap between theoretical efficiency and practical deployment in dynamic environments.
Research Focus
Key Achievements
Top Papers
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