Ruijie Han
Papers
1
Total Citations
1
H-Index
1
About
Ruijie Han is a rising researcher in autonomous systems, with a primary focus on trajectory prediction, multi-agent coordination, and risk-aware decision-making for autonomous driving. Their most notable contribution is the development of a Heterogeneous Multi-Agent Risk-Aware Graph Encoder with a Continuous Parameterized Decoder, a novel framework that addresses the critical challenge of predicting vehicle and pedestrian trajectories in complex, high-risk environments such as intersections. This work integrates graph-based encoding of diverse agent interactions with a parameterized output space, enabling more accurate and safety-conscious predictions. Although early in their career, with their 2024 paper already garnering 1 citation, Han’s research directly tackles real-world bottlenecks in intelligent transportation and human–robot interaction, where understanding collision risks under varied road constraints is paramount. Their approach stands out for its explicit modeling of heterogeneous agents—such as cars, cyclists, and pedestrians—and its continuous parameterization, which enhances prediction fidelity over discrete methods. As autonomous driving technology advances, Han’s work offers a promising pathway toward safer, more reliable navigation in dense, unpredictable environments.
Research Focus
Key Achievements
Top Papers
- 1