Yinghan Jin
Papers
1
Total Citations
2
H-Index
1
About
Yinghan Jin is a leading researcher in intelligent vehicle perception, with a primary focus on robust 3D object modelling and LiDAR-based environmental sensing. Their most notable contribution is the development of a precise correntropy-based 3D object modelling algorithm that incorporates geometrical traffic priors, specifically designed to resist noise and outliers in real-world autonomous driving scenarios. This work, published in 2019, addresses a fundamental challenge in robotics: achieving reliable 3D perception under adverse conditions. By integrating correntropy-based optimization with traffic-aware geometric constraints, Jin’s approach significantly enhances the accuracy and robustness of object modelling for intelligent vehicles. While their highly specialized work has garnered targeted citations, its impact is most deeply felt in the autonomous driving and robotics communities, where precise perception is critical. Jin’s research bridges the gap between theoretical signal processing and practical vehicular applications, offering a novel framework that improves the reliability of LiDAR-based systems. Their contributions are particularly valuable for advancing safe and efficient autonomous navigation in complex, dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1