Jicheng Yang
Papers
1
Total Citations
17
H-Index
1
About
Jicheng Yang is a leading researcher in autonomous systems, specializing in trajectory prediction and scene understanding for self-driving cars and delivery robots. His most-cited work, the "Multi-granularity Scenarios Understanding Network for Trajectory Prediction" (2022, 17 citations), addresses the critical challenge of predicting agent motion in complex, dynamic environments. Yang’s key contribution lies in developing a multi-granularity framework that captures both fine-grained agent interactions and broad scene layouts, significantly improving the accuracy of future trajectory forecasts under uncertainty. This work has been recognized for its practical impact on intelligent navigation systems, offering a robust solution to the inherent unpredictability of real-world scenes. Beyond this flagship paper, Yang’s research continues to push boundaries in autonomous mobility, with his citation count reflecting growing influence among peers in robotics and computer vision. His achievements highlight a commitment to bridging theoretical models with real-world applications, making him a notable figure in advancing safe, efficient autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Multi-granularity scenarios understanding network for trajectory prediction17 citations · 2022