Papers
1
Total Citations
2
H-Index
1
About
Yapeng Ji is a researcher whose work lies at the intersection of computer vision, autonomous driving, and robust perception systems. His primary research focuses on 3D multi-object tracking (MOT), particularly addressing the critical challenge of maintaining reliable performance in adverse weather conditions—a key bottleneck for real-world autonomous systems. In his most cited work, "Robust 3D Multi-Object Tracking in Adverse Weather with Hard Sample Mining" (2023), Ji tackles the severe degradation that existing 3D MOT solutions face in rain, snow, and fog, where missed detections and incorrect data associations become common. His major contribution is a novel framework that leverages hard sample mining strategies to improve tracking robustness under these challenging scenarios. While his citation count is still growing (2 citations for this work), the paper represents an important step toward making autonomous driving and robotics truly weather-resilient. Ji’s research is particularly notable for addressing a practical, real-world problem that is often overlooked in lab settings, and his work has direct implications for the safety and reliability of autonomous vehicles operating in diverse environmental conditions.
Research Focus
Key Achievements
Top Papers
- 1