Chun Jason Xue

City University of Hong Kong, Mohamed bin Zayed University of Artificial Intelligence

Papers

Total Citations

H-Index

About

Chun Jason Xue is a leading researcher in real-time embedded systems and autonomous driving perception, with a focus on the critical intersection of sensor fusion and edge computing. His work addresses fundamental challenges in multi-sensor data fusion for autonomous systems, particularly the synchronization of data from disparate sensor sources with varying sampling times—a problem essential for accurate environmental perception and intelligent decision-making. Xue’s major contributions include the worst-case latency analysis of message synchronization in ROS, providing formal models and property analyses that ensure timing predictability in distributed sensor systems. His research has garnered significant attention, with key papers accumulating citations that underscore their impact on the field. Notably, his work on Moby demonstrates how to empower 2D models for efficient point cloud analytics on edge devices, achieving near real-time 3D object detection with limited computational resources. More recently, his DAWN framework introduces object-aware partitioning and 3D similarity-based filtering to accelerate point cloud detection, further advancing the practicality of autonomous driving and robotics systems. Xue’s contributions are pivotal for enabling reliable, low-latency perception in resource-constrained environments.