Chenxin Xu

About

Chenxin Xu is a rising researcher in autonomous driving and robotics, specializing in self-supervised learning for bird’s eye view (BEV) motion prediction. His work addresses critical challenges in dense motion flow estimation, particularly the issues of fake flow and inconsistency that plague existing point-cloud-based methods. In his highly cited 2024 paper, “Self-Supervised Bird’s Eye View Motion Prediction with Cross-Modality Signals,” Xu introduces a novel framework that leverages cross-modality signals—integrating visual and LiDAR data—to learn robust BEV motion representations without costly manual annotations. This approach significantly improves prediction accuracy and reliability in dynamic driving environments, offering a scalable solution for real-world autonomous systems. With 5 citations already, his work is gaining traction for its practical impact on scene understanding and safety. Xu’s contributions are paving the way for more efficient self-supervised learning pipelines, making him a notable voice in the next generation of autonomous driving research.

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

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