Sehwan Choi
Papers
1
Total Citations
30
H-Index
1
About
Sehwan Choi is a rising researcher in autonomous systems, whose work centers on motion prediction and safe navigation for robots and self-driving vehicles. His most cited study, "R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement" (2023, 30 citations), introduces a novel two-stage framework that leverages tube-query attention to refine trajectory predictions for dynamic agents. This approach significantly enhances the ability to anticipate future motion by integrating scene context and interaction cues, directly addressing critical challenges in risk assessment and motion planning. Choi’s contributions are particularly notable for their focus on safety—a cornerstone of real-world autonomous deployment. By improving prediction accuracy, his work helps bridge the gap between simulation and practical application. With growing recognition in the field, Choi is establishing himself as a key contributor to the next generation of intelligent, context-aware robotic systems.
Research Focus
Key Achievements
Top Papers
- 1