Papers
1
Total Citations
3
H-Index
1
About
Yiyan Wu is a researcher at the forefront of autonomous driving and intelligent transportation systems, with a primary focus on enhancing vehicle perception and safety through multimodal sensor fusion. Wu’s most-cited work, "Novel Moving-Target Detection Using A Hybrid of RGB Images and LiDAR Point-Clouds" (2020), tackles a critical challenge in self-driving technology: the real-time detection of moving objects like pedestrians and vehicles, which pose high risks to driverless cars. By integrating visual data from RGB cameras with 3D LiDAR point clouds, Wu developed a hybrid approach that significantly improves the accuracy and speed of moving-target detection, directly addressing public-safety concerns in future intelligent transportation systems. While this paper has garnered 3 citations, it represents a foundational contribution to the field, demonstrating Wu’s expertise in combining computer vision and LiDAR processing for robust scene analysis. Wu’s work is particularly notable for its practical implications, aiming to reduce collision risks and enhance the reliability of autonomous navigation. This research positions Wu as a promising innovator in the intersection of sensor fusion, object tracking, and real-time decision-making, with potential to shape safer, smarter transportation networks.
Research Focus
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Top Papers
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