Papers
8
Total Citations
76
H-Index
4
About
Shichao Wu is a researcher advancing the frontiers of autonomous systems, with a primary focus on human trajectory prediction, 3D pedestrian detection, and acoustic-based person identification. Their most impactful work, "GA-STT: Human Trajectory Prediction With Group Aware Spatial-Temporal Transformer" (2022, 31 citations), introduces a novel transformer architecture that models socially aware spatial interactions and complex temporal dependencies in crowds—a critical capability for robotics and autonomous driving. Wu has also made significant contributions to multimodal sensing, developing the AFPILD dataset (2023, 12 citations) and pioneering acoustic footstep-based person identification through advanced feature fusion techniques, achieving robust performance even with sparse or deformed data. In LiDAR-based perception, their RPEA (2024, 10 citations) and DCCLA (2024) papers propose efficient attention mechanisms and dense cross connections to overcome challenges posed by pedestrian point cloud sparsity and body posture variations. Additionally, Wu has explored brain-computer interfaces (SSVEP-based robot control, 2018) and motion planning efficiency (2023), demonstrating a broad technical range. With over 70 total citations across their publications, Wu’s work directly addresses real-world challenges in autonomous navigation and human-robot interaction.
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