Jiangyang Yu

Papers

Total Citations

H-Index

About

Jiangyang Yu is a researcher at the forefront of autonomous systems, with a primary focus on intelligent path planning and multi-modal perception for self-driving vehicles. His work addresses the critical challenge of enabling safe and efficient navigation in complex, dynamic urban environments. Yu’s most cited contribution, "Multi-Modal Neural Feature Fusion for Automatic Driving Through Perception-Aware Path Planning" (2021), with 18 citations, introduces a novel framework that integrates data from diverse sensor modalities—such as cameras and LiDAR—to enhance obstacle detection and path planning robustness. This perception-aware approach represents a significant advancement over traditional methods, directly improving the reliability of autonomous driving, robotic navigation, and aircraft tracking in cluttered scenes. By fusing neural features with path planning algorithms, Yu’s research bridges the gap between environmental understanding and decision-making, offering a more holistic solution for real-world deployment. His work is particularly notable for its practical implications, addressing the core need for accuracy and adaptability in rapidly changing road conditions. As the field moves toward fully autonomous mobility, Yu’s contributions provide a foundational step toward safer, more intelligent vehicles.