Papers
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Total Citations
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H-Index
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About
Jie Hao is a rising researcher in autonomous driving perception, with a focus on leveraging 4D radar technology for robust 3D object detection. Their key research areas include multi-modal sensor fusion, deep learning for point cloud processing, and attention-based neural architectures. Hao’s most notable contribution is the development of RMSA-Net, a 4D radar-based multi-scale attention network that addresses the critical challenge of object detection in adverse weather and low-light conditions where traditional cameras and LiDAR often fail. By exploiting the high angular resolution of 4D radar in both azimuth and elevation, RMSA-Net enhances spatial feature extraction through multi-scale attention mechanisms, improving detection accuracy for autonomous driving and robotic systems. Although early in their career, with RMSA-Net already garnering citations, Hao’s work signals a promising shift toward more resilient perception systems. Their research bridges the gap between radar’s robustness and the precision needed for safe autonomous navigation, positioning Hao as a key contributor to next-generation sensing solutions.
Research Focus
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Top Papers
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