Yiqi Zhong

About

Yiqi Zhong is a rising researcher at the forefront of autonomous driving and robotics, with a sharp focus on self-supervised learning for bird’s eye view (BEV) perception and motion prediction. His most cited work, “Self-Supervised Bird’s Eye View Motion Prediction with Cross-Modality Signals” (2024, 5 citations), tackles a critical challenge in the field: learning dense BEV motion flows without costly manual labels. Zhong’s key contribution lies in addressing the limitations of existing self-supervised methods, which often suffer from fake flow and inconsistency due to reliance on point cloud correspondences. By introducing cross-modality signals—likely fusing camera and LiDAR data—he proposes a more robust framework that improves motion prediction accuracy and reliability. This work has immediate implications for safer autonomous navigation, where understanding dynamic scene motion is essential. Though early in his career, Zhong’s approach signals a promising direction for scalable, annotation-free perception systems. His research stands out for its technical rigor and practical relevance, making him a name to watch in the autonomous driving community.

Research Focus

Key Achievements

Top Papers

1. 1
   Self-Supervised Bird’s Eye View Motion Prediction with Cross-Modality Signals

   5 citations · 2024

Key Collaborators

Contact & Links