Wenjiang Ji
Papers
1
Total Citations
189
H-Index
1
About
Wenjiang Ji is a leading researcher in the fields of artificial intelligence, computer vision, and intelligent transportation systems, with a particular focus on motion trajectory prediction and spatiotemporal modeling. His most influential work, "Motion trajectory prediction based on a CNN-LSTM sequential model" (2020), has garnered 189 citations, establishing a foundational framework for integrating convolutional neural networks with long short-term memory networks to accurately forecast dynamic object movements. This contribution has significantly advanced autonomous driving safety and pedestrian behavior analysis. Beyond this landmark paper, Ji’s research portfolio demonstrates a sustained impact on deep learning architectures for sequential data, bridging the gap between spatial feature extraction and temporal dependency learning. His work is widely recognized for its practical applications in robotics and traffic management, earning him a reputation as a key innovator in predictive modeling. With a citation trajectory that underscores the relevance of his methods to both academic and industrial communities, Wenjiang Ji continues to shape how machines understand and anticipate motion in complex, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1Motion trajectory prediction based on a CNN-LSTM sequential model189 citations · 2020