Jing Lian
Papers
1
Total Citations
17
H-Index
1
About
Jing Lian is a leading researcher in autonomous vehicle perception and pedestrian behavior prediction, with a focus on enhancing safety in dynamic environments. His work centers on developing advanced trajectory forecasting models that integrate causal reasoning with temporal–spatial analysis. In his highly cited 2022 paper, “Causal Temporal–Spatial Pedestrian Trajectory Prediction With Goal Point Estimation and Contextual Interaction,” Lian introduced the CTSGI model, which leverages self-attention mechanisms to capture complex interactions between pedestrians and their surroundings. This model significantly improves prediction accuracy by estimating goal points and contextual cues, addressing a critical challenge for autonomous vehicles and robots navigating crowded spaces. With 17 citations and growing recognition, Lian’s contributions are shaping the future of intelligent transportation systems. His innovative approach to combining causality with deep learning has established him as a key figure in pedestrian safety research, offering practical solutions for real-world autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1