Xuanqi Lin
Papers
1
Total Citations
3
H-Index
1
About
Xuanqi Lin is a rising researcher in intelligent autonomous systems, with a primary focus on multiagent trajectory prediction and deep learning for behavior modeling. His most notable contribution is the development of a global-local scene-enhanced social interaction graph network, a novel framework that significantly improves the accuracy of trajectory forecasting by capturing both broad environmental context and fine-grained social interactions among agents. This work, published in 2024, has already garnered 3 citations, signaling its early impact in the field of autonomous driving and service robotics. Lin's research addresses a critical challenge in autonomous systems: enabling vehicles and robots to anticipate the future movements of pedestrians, cyclists, and other agents in complex, dynamic scenes. By integrating scene-level information with social interaction graphs, his approach offers a more holistic understanding of multiagent behavior. As a researcher dedicated to advancing the safety and efficiency of autonomous technologies, Lin's work is poised to influence future developments in trajectory prediction, with potential applications ranging from self-driving cars to intelligent surveillance and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1