Xuelian Liu
Papers
2
Total Citations
6
H-Index
1
About
Xuelian Liu is a researcher advancing the frontiers of autonomous systems, with key contributions in 3D perception, trajectory prediction, and intelligent transportation. Their work primarily focuses on LiDAR point cloud object recognition and multi-agent risk-aware modeling for autonomous driving. Liu’s most-cited paper, “Point cloud object recognition method via histograms of dual deviation angle feature” (2023, 5 citations), introduces a novel feature descriptor that enhances 3D object recognition in applications ranging from remote sensing to robotics. This method addresses critical challenges in real-world perception by improving accuracy in cluttered environments. In their more recent work, “Heterogeneous Multi-Agent Risk-Aware Graph Encoder with Continuous Parameterized Decoder for Autonomous Driving Trajectory Prediction” (2024, 1 citation), Liu tackles the complex problem of predicting trajectories at intersections, where diverse road users and interactions create high collision risks. By integrating graph-based encoding with continuous parameterization, this approach offers a robust framework for safer autonomous navigation. Liu’s research is pivotal for developing reliable perception and prediction systems, directly impacting the safety and efficiency of autonomous vehicles and human-robot collaboration. Their work continues to shape how machines understand and navigate dynamic, multi-agent environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2